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human_written_ref/3D Object Detection for Autonomous Driving: A Comprehensive Survey.json
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| 1 |
+
{
|
| 2 |
+
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|
| 3 |
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"arxivId": "1512.03385",
|
| 4 |
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"title": "Deep Residual Learning for Image Recognition"
|
| 5 |
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|
| 6 |
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| 7 |
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|
| 8 |
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|
| 9 |
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|
| 10 |
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|
| 11 |
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|
| 12 |
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"title": "U-Net: Convolutional Networks for Biomedical Image Segmentation"
|
| 13 |
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|
| 14 |
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|
| 15 |
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|
| 16 |
+
"title": "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks"
|
| 17 |
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|
| 18 |
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|
| 19 |
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|
| 20 |
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|
| 21 |
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|
| 22 |
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|
| 23 |
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|
| 24 |
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|
| 25 |
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|
| 26 |
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|
| 27 |
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|
| 28 |
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|
| 29 |
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|
| 30 |
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|
| 31 |
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|
| 32 |
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|
| 33 |
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|
| 34 |
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| 35 |
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| 36 |
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| 37 |
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| 38 |
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| 39 |
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| 40 |
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|
| 41 |
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| 42 |
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| 43 |
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|
| 44 |
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| 45 |
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| 46 |
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| 47 |
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|
| 48 |
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|
| 49 |
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|
| 50 |
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| 51 |
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| 52 |
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| 53 |
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|
| 54 |
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| 55 |
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|
| 56 |
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| 57 |
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|
| 58 |
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| 59 |
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| 60 |
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| 61 |
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| 62 |
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| 63 |
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|
| 64 |
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|
| 65 |
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| 66 |
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| 67 |
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| 68 |
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|
| 69 |
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| 70 |
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| 71 |
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| 72 |
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| 73 |
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|
| 74 |
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| 75 |
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| 76 |
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| 77 |
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| 78 |
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| 79 |
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|
| 80 |
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|
| 81 |
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|
| 82 |
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| 83 |
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|
| 84 |
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|
| 85 |
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|
| 86 |
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| 87 |
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| 88 |
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| 89 |
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| 90 |
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| 91 |
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| 92 |
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| 93 |
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|
| 94 |
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|
| 95 |
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|
| 96 |
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|
| 97 |
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|
| 98 |
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|
| 99 |
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|
| 100 |
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|
| 101 |
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|
| 102 |
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| 103 |
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|
| 104 |
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|
| 105 |
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| 106 |
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| 107 |
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| 108 |
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"title": "Multi-view 3D Object Detection Network for Autonomous Driving"
|
| 109 |
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|
| 110 |
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| 111 |
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"arxivId": "1512.02134",
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| 112 |
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"title": "A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation"
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| 113 |
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|
| 114 |
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|
| 115 |
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"arxivId": "1912.04838",
|
| 116 |
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"title": "Scalability in Perception for Autonomous Driving: Waymo Open Dataset"
|
| 117 |
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|
| 118 |
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|
| 119 |
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"arxivId": "1812.04244",
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| 120 |
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"title": "PointRCNN: 3D Object Proposal Generation and Detection From Point Cloud"
|
| 121 |
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|
| 122 |
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| 123 |
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"arxivId": "1711.08488",
|
| 124 |
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"title": "Frustum PointNets for 3D Object Detection from RGB-D Data"
|
| 125 |
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|
| 126 |
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|
| 127 |
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"arxivId": "1705.05065",
|
| 128 |
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"title": "AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles"
|
| 129 |
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|
| 130 |
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| 131 |
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| 132 |
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| 133 |
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| 134 |
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| 135 |
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|
| 136 |
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"title": "PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection"
|
| 137 |
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|
| 138 |
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| 139 |
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|
| 140 |
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"title": "Deep Learning for 3D Point Clouds: A Survey"
|
| 141 |
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|
| 142 |
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|
| 143 |
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"arxivId": "1711.10275",
|
| 144 |
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"title": "3D Semantic Segmentation with Submanifold Sparse Convolutional Networks"
|
| 145 |
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|
| 146 |
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|
| 147 |
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|
| 148 |
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|
| 149 |
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|
| 150 |
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| 151 |
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|
| 152 |
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|
| 153 |
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|
| 154 |
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|
| 155 |
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|
| 156 |
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"title": "Center-based 3D Object Detection and Tracking"
|
| 157 |
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|
| 158 |
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|
| 159 |
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|
| 160 |
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"title": "Joint 3D Proposal Generation and Object Detection from View Aggregation"
|
| 161 |
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|
| 162 |
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|
| 163 |
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"arxivId": "1707.06484",
|
| 164 |
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"title": "Deep Layer Aggregation"
|
| 165 |
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|
| 166 |
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|
| 167 |
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"arxivId": "1911.02620",
|
| 168 |
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"title": "Argoverse: 3D Tracking and Forecasting With Rich Maps"
|
| 169 |
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|
| 170 |
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"1904.09664": {
|
| 171 |
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"arxivId": "1904.09664",
|
| 172 |
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"title": "Deep Hough Voting for 3D Object Detection in Point Clouds"
|
| 173 |
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|
| 174 |
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|
| 175 |
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"arxivId": "1902.06326",
|
| 176 |
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"title": "PIXOR: Real-time 3D Object Detection from Point Clouds"
|
| 177 |
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|
| 178 |
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|
| 179 |
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"arxivId": "1710.02410",
|
| 180 |
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"title": "End-to-End Driving Via Conditional Imitation Learning"
|
| 181 |
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|
| 182 |
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|
| 183 |
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|
| 184 |
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"title": "BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers"
|
| 185 |
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|
| 186 |
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|
| 187 |
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|
| 188 |
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"title": "3D Bounding Box Estimation Using Deep Learning and Geometry"
|
| 189 |
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|
| 190 |
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|
| 191 |
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"arxivId": "1812.07179",
|
| 192 |
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"title": "Pseudo-LiDAR From Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving"
|
| 193 |
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|
| 194 |
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|
| 195 |
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"arxivId": "2002.10187",
|
| 196 |
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"title": "3DSSD: Point-Based 3D Single Stage Object Detector"
|
| 197 |
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|
| 198 |
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|
| 199 |
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|
| 200 |
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"title": "Lift, Splat, Shoot: Encoding Images From Arbitrary Camera Rigs by Implicitly Unprojecting to 3D"
|
| 201 |
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|
| 202 |
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|
| 203 |
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"arxivId": "2012.10992",
|
| 204 |
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"title": "Deep Continuous Fusion for Multi-sensor 3D Object Detection"
|
| 205 |
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|
| 206 |
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|
| 207 |
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"arxivId": "1907.03670",
|
| 208 |
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"title": "From Points to Parts: 3D Object Detection From Point Cloud With Part-Aware and Part-Aggregation Network"
|
| 209 |
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|
| 210 |
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|
| 211 |
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"arxivId": "2012.15712",
|
| 212 |
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"title": "Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection"
|
| 213 |
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},
|
| 214 |
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|
| 215 |
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"arxivId": "1907.10471",
|
| 216 |
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"title": "STD: Sparse-to-Dense 3D Object Detector for Point Cloud"
|
| 217 |
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},
|
| 218 |
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|
| 1239 |
+
"arxivId": "2203.08332",
|
| 1240 |
+
"title": "WeakM3D: Towards Weakly Supervised Monocular 3D Object Detection"
|
| 1241 |
+
},
|
| 1242 |
+
"2011.06425": {
|
| 1243 |
+
"arxivId": "2011.06425",
|
| 1244 |
+
"title": "StrObe: Streaming Object Detection from LiDAR Packets"
|
| 1245 |
+
},
|
| 1246 |
+
"2005.10863": {
|
| 1247 |
+
"arxivId": "2005.10863",
|
| 1248 |
+
"title": "RV-FuseNet: Range View Based Fusion of Time-Series LiDAR Data for Joint 3D Object Detection and Motion Forecasting"
|
| 1249 |
+
},
|
| 1250 |
+
"2203.13394": {
|
| 1251 |
+
"arxivId": "2203.13394",
|
| 1252 |
+
"title": "Point2Seq: Detecting 3D Objects as Sequences"
|
| 1253 |
+
},
|
| 1254 |
+
"2006.16007": {
|
| 1255 |
+
"arxivId": "2006.16007",
|
| 1256 |
+
"title": "MoNet3D: Towards Accurate Monocular 3D Object Localization in Real Time"
|
| 1257 |
+
},
|
| 1258 |
+
"2110.09355": {
|
| 1259 |
+
"arxivId": "2110.09355",
|
| 1260 |
+
"title": "FAST3D: Flow-Aware Self-Training for 3D Object Detectors"
|
| 1261 |
+
}
|
| 1262 |
+
}
|
human_written_ref/Graph neural networks: Taxonomy, advances, and trends.json
ADDED
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@@ -0,0 +1,1382 @@
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|
| 1 |
+
{
|
| 2 |
+
"1512.03385": {
|
| 3 |
+
"arxivId": "1512.03385",
|
| 4 |
+
"title": "Deep Residual Learning for Image Recognition"
|
| 5 |
+
},
|
| 6 |
+
"1706.03762": {
|
| 7 |
+
"arxivId": "1706.03762",
|
| 8 |
+
"title": "Attention is All you Need"
|
| 9 |
+
},
|
| 10 |
+
"1810.04805": {
|
| 11 |
+
"arxivId": "1810.04805",
|
| 12 |
+
"title": "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding"
|
| 13 |
+
},
|
| 14 |
+
"1505.04597": {
|
| 15 |
+
"arxivId": "1505.04597",
|
| 16 |
+
"title": "U-Net: Convolutional Networks for Biomedical Image Segmentation"
|
| 17 |
+
},
|
| 18 |
+
"1409.4842": {
|
| 19 |
+
"arxivId": "1409.4842",
|
| 20 |
+
"title": "Going deeper with convolutions"
|
| 21 |
+
},
|
| 22 |
+
"1912.01703": {
|
| 23 |
+
"arxivId": "1912.01703",
|
| 24 |
+
"title": "PyTorch: An Imperative Style, High-Performance Deep Learning Library"
|
| 25 |
+
},
|
| 26 |
+
"1608.06993": {
|
| 27 |
+
"arxivId": "1608.06993",
|
| 28 |
+
"title": "Densely Connected Convolutional Networks"
|
| 29 |
+
},
|
| 30 |
+
"1409.0473": {
|
| 31 |
+
"arxivId": "1409.0473",
|
| 32 |
+
"title": "Neural Machine Translation by Jointly Learning to Align and Translate"
|
| 33 |
+
},
|
| 34 |
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"1609.02907": {
|
| 35 |
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"arxivId": "1609.02907",
|
| 36 |
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"title": "Semi-Supervised Classification with Graph Convolutional Networks"
|
| 37 |
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},
|
| 38 |
+
"1406.1078": {
|
| 39 |
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"arxivId": "1406.1078",
|
| 40 |
+
"title": "Learning Phrase Representations using RNN Encoder\u2013Decoder for Statistical Machine Translation"
|
| 41 |
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},
|
| 42 |
+
"1710.10903": {
|
| 43 |
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"arxivId": "1710.10903",
|
| 44 |
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"title": "Graph Attention Networks"
|
| 45 |
+
},
|
| 46 |
+
"1706.02216": {
|
| 47 |
+
"arxivId": "1706.02216",
|
| 48 |
+
"title": "Inductive Representation Learning on Large Graphs"
|
| 49 |
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},
|
| 50 |
+
"1607.00653": {
|
| 51 |
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"arxivId": "1607.00653",
|
| 52 |
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"title": "node2vec: Scalable Feature Learning for Networks"
|
| 53 |
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},
|
| 54 |
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"1403.6652": {
|
| 55 |
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"arxivId": "1403.6652",
|
| 56 |
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"title": "DeepWalk: online learning of social representations"
|
| 57 |
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},
|
| 58 |
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"1710.09412": {
|
| 59 |
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"arxivId": "1710.09412",
|
| 60 |
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"title": "mixup: Beyond Empirical Risk Minimization"
|
| 61 |
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},
|
| 62 |
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"1711.07971": {
|
| 63 |
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"arxivId": "1711.07971",
|
| 64 |
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"title": "Non-local Neural Networks"
|
| 65 |
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},
|
| 66 |
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"1511.07122": {
|
| 67 |
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"arxivId": "1511.07122",
|
| 68 |
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"title": "Multi-Scale Context Aggregation by Dilated Convolutions"
|
| 69 |
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},
|
| 70 |
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"1508.04025": {
|
| 71 |
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"arxivId": "1508.04025",
|
| 72 |
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"title": "Effective Approaches to Attention-based Neural Machine Translation"
|
| 73 |
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},
|
| 74 |
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"1901.00596": {
|
| 75 |
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"arxivId": "1901.00596",
|
| 76 |
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"title": "A Comprehensive Survey on Graph Neural Networks"
|
| 77 |
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},
|
| 78 |
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"1606.09375": {
|
| 79 |
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"arxivId": "1606.09375",
|
| 80 |
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"title": "Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering"
|
| 81 |
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},
|
| 82 |
+
"1704.01212": {
|
| 83 |
+
"arxivId": "1704.01212",
|
| 84 |
+
"title": "Neural Message Passing for Quantum Chemistry"
|
| 85 |
+
},
|
| 86 |
+
"1810.00826": {
|
| 87 |
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"arxivId": "1810.00826",
|
| 88 |
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"title": "How Powerful are Graph Neural Networks?"
|
| 89 |
+
},
|
| 90 |
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"1801.07829": {
|
| 91 |
+
"arxivId": "1801.07829",
|
| 92 |
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"title": "Dynamic Graph CNN for Learning on Point Clouds"
|
| 93 |
+
},
|
| 94 |
+
"1812.08434": {
|
| 95 |
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"arxivId": "1812.08434",
|
| 96 |
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"title": "Graph Neural Networks: A Review of Methods and Applications"
|
| 97 |
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},
|
| 98 |
+
"1312.6203": {
|
| 99 |
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"arxivId": "1312.6203",
|
| 100 |
+
"title": "Spectral Networks and Locally Connected Networks on Graphs"
|
| 101 |
+
},
|
| 102 |
+
"1601.00670": {
|
| 103 |
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"arxivId": "1601.00670",
|
| 104 |
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"title": "Variational Inference: A Review for Statisticians"
|
| 105 |
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},
|
| 106 |
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"1710.09829": {
|
| 107 |
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"arxivId": "1710.09829",
|
| 108 |
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"title": "Dynamic Routing Between Capsules"
|
| 109 |
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},
|
| 110 |
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"1703.06103": {
|
| 111 |
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"arxivId": "1703.06103",
|
| 112 |
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"title": "Modeling Relational Data with Graph Convolutional Networks"
|
| 113 |
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},
|
| 114 |
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"1211.0053": {
|
| 115 |
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"arxivId": "1211.0053",
|
| 116 |
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"title": "The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains"
|
| 117 |
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},
|
| 118 |
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"1903.02428": {
|
| 119 |
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"arxivId": "1903.02428",
|
| 120 |
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"title": "Fast Graph Representation Learning with PyTorch Geometric"
|
| 121 |
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},
|
| 122 |
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"1801.07455": {
|
| 123 |
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"arxivId": "1801.07455",
|
| 124 |
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"title": "Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition"
|
| 125 |
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},
|
| 126 |
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"1406.6247": {
|
| 127 |
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"arxivId": "1406.6247",
|
| 128 |
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"title": "Recurrent Models of Visual Attention"
|
| 129 |
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},
|
| 130 |
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"1509.09292": {
|
| 131 |
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"arxivId": "1509.09292",
|
| 132 |
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"title": "Convolutional Networks on Graphs for Learning Molecular Fingerprints"
|
| 133 |
+
},
|
| 134 |
+
"1806.01973": {
|
| 135 |
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"arxivId": "1806.01973",
|
| 136 |
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"title": "Graph Convolutional Neural Networks for Web-Scale Recommender Systems"
|
| 137 |
+
},
|
| 138 |
+
"1611.07308": {
|
| 139 |
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"arxivId": "1611.07308",
|
| 140 |
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"title": "Variational Graph Auto-Encoders"
|
| 141 |
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},
|
| 142 |
+
"1511.05493": {
|
| 143 |
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"arxivId": "1511.05493",
|
| 144 |
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"title": "Gated Graph Sequence Neural Networks"
|
| 145 |
+
},
|
| 146 |
+
"1709.04875": {
|
| 147 |
+
"arxivId": "1709.04875",
|
| 148 |
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"title": "Spatio-temporal Graph Convolutional Neural Network: A Deep Learning Framework for Traffic Forecasting"
|
| 149 |
+
},
|
| 150 |
+
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| 1223 |
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|
| 1224 |
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| 1225 |
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| 1226 |
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| 1227 |
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| 1228 |
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| 1229 |
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| 1230 |
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| 1231 |
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|
| 1232 |
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| 1233 |
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| 1234 |
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| 1235 |
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| 1236 |
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| 1237 |
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| 1238 |
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| 1239 |
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| 1240 |
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| 1241 |
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| 1242 |
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| 1243 |
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| 1244 |
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| 1245 |
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| 1246 |
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| 1247 |
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| 1251 |
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| 1253 |
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| 1267 |
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| 1269 |
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| 1270 |
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| 1271 |
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| 1272 |
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| 1275 |
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| 1281 |
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| 1283 |
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| 1291 |
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| 1293 |
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| 1294 |
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| 1295 |
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| 1297 |
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| 1299 |
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| 1303 |
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| 1315 |
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| 1316 |
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| 1319 |
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| 1327 |
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| 1339 |
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| 1340 |
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| 1343 |
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| 1345 |
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| 1346 |
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| 1347 |
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| 1348 |
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| 1349 |
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| 1350 |
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| 1351 |
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| 1352 |
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| 1353 |
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| 1355 |
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| 1356 |
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|
| 1357 |
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| 1359 |
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| 1360 |
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|
| 1361 |
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| 1362 |
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|
| 1363 |
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| 1364 |
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| 1365 |
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| 1366 |
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| 1367 |
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| 1368 |
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|
| 1369 |
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| 1370 |
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|
| 1371 |
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|
| 1372 |
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|
| 1373 |
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| 1374 |
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| 1375 |
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| 1376 |
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|
| 1377 |
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| 1378 |
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| 1379 |
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| 1380 |
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|
| 1381 |
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|
| 1382 |
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|
human_written_ref/Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models.json
ADDED
|
@@ -0,0 +1,746 @@
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| 1 |
+
{
|
| 2 |
+
"1706.03762": {
|
| 3 |
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"arxivId": "1706.03762",
|
| 4 |
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"title": "Attention is All you Need"
|
| 5 |
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|
| 6 |
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|
| 7 |
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|
| 8 |
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"title": "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding"
|
| 9 |
+
},
|
| 10 |
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"2005.14165": {
|
| 11 |
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|
| 12 |
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"title": "Language Models are Few-Shot Learners"
|
| 13 |
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},
|
| 14 |
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|
| 15 |
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|
| 16 |
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"title": "RoBERTa: A Robustly Optimized BERT Pretraining Approach"
|
| 17 |
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|
| 18 |
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|
| 19 |
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|
| 20 |
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|
| 21 |
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|
| 22 |
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|
| 23 |
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|
| 24 |
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"title": "Proximal Policy Optimization Algorithms"
|
| 25 |
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|
| 26 |
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|
| 27 |
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"arxivId": "1910.13461",
|
| 28 |
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"title": "BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension"
|
| 29 |
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|
| 30 |
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|
| 31 |
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|
| 32 |
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|
| 33 |
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|
| 34 |
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|
| 35 |
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|
| 36 |
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|
| 37 |
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|
| 38 |
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|
| 39 |
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|
| 40 |
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"title": "GPT-4 Technical Report"
|
| 41 |
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|
| 42 |
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|
| 43 |
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|
| 44 |
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"title": "Llama 2: Open Foundation and Fine-Tuned Chat Models"
|
| 45 |
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|
| 46 |
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|
| 47 |
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"arxivId": "1909.11942",
|
| 48 |
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"title": "ALBERT: A Lite BERT for Self-supervised Learning of Language Representations"
|
| 49 |
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|
| 50 |
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|
| 51 |
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|
| 52 |
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"title": "Chain of Thought Prompting Elicits Reasoning in Large Language Models"
|
| 53 |
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|
| 54 |
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|
| 55 |
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"arxivId": "1706.04599",
|
| 56 |
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|
| 57 |
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|
| 58 |
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|
| 59 |
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|
| 60 |
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|
| 61 |
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|
| 62 |
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|
| 63 |
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"arxivId": "1904.09675",
|
| 64 |
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|
| 65 |
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|
| 66 |
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|
| 67 |
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|
| 68 |
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"title": "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks"
|
| 69 |
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|
| 70 |
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|
| 71 |
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"arxivId": "2109.01652",
|
| 72 |
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"title": "Finetuned Language Models Are Zero-Shot Learners"
|
| 73 |
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|
| 74 |
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|
| 75 |
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|
| 76 |
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"title": "The Curious Case of Neural Text Degeneration"
|
| 77 |
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|
| 78 |
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|
| 79 |
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"arxivId": "2210.11416",
|
| 80 |
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"title": "Scaling Instruction-Finetuned Language Models"
|
| 81 |
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|
| 82 |
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|
| 83 |
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|
| 84 |
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"title": "Visual Instruction Tuning"
|
| 85 |
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|
| 86 |
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|
| 87 |
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"arxivId": "2203.11171",
|
| 88 |
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"title": "Self-Consistency Improves Chain of Thought Reasoning in Language Models"
|
| 89 |
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|
| 90 |
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|
| 91 |
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|
| 92 |
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"title": "BLOOM: A 176B-Parameter Open-Access Multilingual Language Model"
|
| 93 |
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|
| 94 |
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|
| 95 |
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"arxivId": "2303.18223",
|
| 96 |
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"title": "A Survey of Large Language Models"
|
| 97 |
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|
| 98 |
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|
| 99 |
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|
| 100 |
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|
| 101 |
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|
| 102 |
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|
| 103 |
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"arxivId": "2212.10560",
|
| 104 |
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"title": "Self-Instruct: Aligning Language Models with Self-Generated Instructions"
|
| 105 |
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|
| 106 |
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|
| 107 |
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"arxivId": "2202.03629",
|
| 108 |
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"title": "Survey of Hallucination in Natural Language Generation"
|
| 109 |
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|
| 110 |
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|
| 111 |
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"arxivId": "2210.03629",
|
| 112 |
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"title": "ReAct: Synergizing Reasoning and Acting in Language Models"
|
| 113 |
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|
| 114 |
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"2003.08271": {
|
| 115 |
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"arxivId": "2003.08271",
|
| 116 |
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"title": "Pre-trained models for natural language processing: A survey"
|
| 117 |
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|
| 118 |
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|
| 119 |
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"arxivId": "2109.07958",
|
| 120 |
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"title": "TruthfulQA: Measuring How Models Mimic Human Falsehoods"
|
| 121 |
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|
| 122 |
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|
| 123 |
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"arxivId": "1611.04230",
|
| 124 |
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"title": "SummaRuNNer: A Recurrent Neural Network Based Sequence Model for Extractive Summarization of Documents"
|
| 125 |
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|
| 126 |
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|
| 127 |
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"arxivId": "2304.03442",
|
| 128 |
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"title": "Generative Agents: Interactive Simulacra of Human Behavior"
|
| 129 |
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|
| 130 |
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|
| 131 |
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"arxivId": "2302.04023",
|
| 132 |
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"title": "A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity"
|
| 133 |
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},
|
| 134 |
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|
| 135 |
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"arxivId": "2210.02414",
|
| 136 |
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"title": "GLM-130B: An Open Bilingual Pre-trained Model"
|
| 137 |
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|
| 138 |
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|
| 139 |
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"arxivId": "2112.09332",
|
| 140 |
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"title": "WebGPT: Browser-assisted question-answering with human feedback"
|
| 141 |
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|
| 142 |
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|
| 143 |
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"arxivId": "2307.03172",
|
| 144 |
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"title": "Lost in the Middle: How Language Models Use Long Contexts"
|
| 145 |
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|
| 146 |
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|
| 147 |
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"arxivId": "2202.05262",
|
| 148 |
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"title": "Locating and Editing Factual Associations in GPT"
|
| 149 |
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},
|
| 150 |
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"2005.00661": {
|
| 151 |
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"arxivId": "2005.00661",
|
| 152 |
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"title": "On Faithfulness and Factuality in Abstractive Summarization"
|
| 153 |
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},
|
| 154 |
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|
| 155 |
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"arxivId": "2307.15043",
|
| 156 |
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"title": "Universal and Transferable Adversarial Attacks on Aligned Language Models"
|
| 157 |
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|
| 158 |
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|
| 159 |
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"arxivId": "2112.04426",
|
| 160 |
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"title": "Improving language models by retrieving from trillions of tokens"
|
| 161 |
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|
| 162 |
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|
| 163 |
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"arxivId": "2307.03109",
|
| 164 |
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"title": "A Survey on Evaluation of Large Language Models"
|
| 165 |
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},
|
| 166 |
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|
| 167 |
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"arxivId": "2002.08910",
|
| 168 |
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"title": "How Much Knowledge Can You Pack into the Parameters of a Language Model?"
|
| 169 |
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},
|
| 170 |
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"2111.01243": {
|
| 171 |
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"arxivId": "2111.01243",
|
| 172 |
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"title": "Recent Advances in Natural Language Processing via Large Pre-trained Language Models: A Survey"
|
| 173 |
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},
|
| 174 |
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|
| 175 |
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"arxivId": "2304.14178",
|
| 176 |
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"title": "mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality"
|
| 177 |
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|
| 178 |
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|
| 179 |
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"arxivId": "1711.01731",
|
| 180 |
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"title": "A Survey on Dialogue Systems: Recent Advances and New Frontiers"
|
| 181 |
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|
| 182 |
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|
| 183 |
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"arxivId": "1910.12840",
|
| 184 |
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"title": "Evaluating the Factual Consistency of Abstractive Text Summarization"
|
| 185 |
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|
| 186 |
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|
| 187 |
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"arxivId": "1805.01954",
|
| 188 |
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"title": "Behavioral Cloning from Observation"
|
| 189 |
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|
| 190 |
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|
| 191 |
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"arxivId": "2306.01116",
|
| 192 |
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"title": "The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only"
|
| 193 |
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|
| 194 |
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|
| 195 |
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|
| 196 |
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"title": "LIMA: Less Is More for Alignment"
|
| 197 |
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|
| 198 |
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|
| 199 |
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|
| 200 |
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"title": "Voyager: An Open-Ended Embodied Agent with Large Language Models"
|
| 201 |
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|
| 202 |
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|
| 203 |
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|
| 204 |
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"title": "Jailbroken: How Does LLM Safety Training Fail?"
|
| 205 |
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|
| 206 |
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|
| 207 |
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"arxivId": "2207.05221",
|
| 208 |
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"title": "Language Models (Mostly) Know What They Know"
|
| 209 |
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|
| 210 |
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|
| 211 |
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"arxivId": "2304.03277",
|
| 212 |
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"title": "Instruction Tuning with GPT-4"
|
| 213 |
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|
| 214 |
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|
| 215 |
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|
| 216 |
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"title": "OpenAssistant Conversations - Democratizing Large Language Model Alignment"
|
| 217 |
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|
| 218 |
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|
| 219 |
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"arxivId": "2301.12652",
|
| 220 |
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"title": "REPLUG: Retrieval-Augmented Black-Box Language Models"
|
| 221 |
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|
| 222 |
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|
| 223 |
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|
| 224 |
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"title": "Editing Factual Knowledge in Language Models"
|
| 225 |
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|
| 226 |
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|
| 227 |
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|
| 228 |
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"title": "Evaluating Object Hallucination in Large Vision-Language Models"
|
| 229 |
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|
| 230 |
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|
| 231 |
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|
| 232 |
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"title": "FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation"
|
| 233 |
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|
| 234 |
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|
| 235 |
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|
| 236 |
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"title": "Large Language Models Can Be Easily Distracted by Irrelevant Context"
|
| 237 |
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|
| 238 |
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|
| 239 |
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|
| 240 |
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"title": "Let's Verify Step by Step"
|
| 241 |
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|
| 242 |
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|
| 243 |
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|
| 244 |
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"title": "Mass-Editing Memory in a Transformer"
|
| 245 |
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|
| 246 |
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|
| 247 |
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"arxivId": "2305.14325",
|
| 248 |
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"title": "Improving Factuality and Reasoning in Language Models through Multiagent Debate"
|
| 249 |
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|
| 250 |
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|
| 251 |
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"arxivId": "2005.03754",
|
| 252 |
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"title": "FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive Summarization"
|
| 253 |
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|
| 254 |
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|
| 255 |
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"arxivId": "2302.00083",
|
| 256 |
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"title": "In-Context Retrieval-Augmented Language Models"
|
| 257 |
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|
| 258 |
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|
| 259 |
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"arxivId": "2112.07899",
|
| 260 |
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"title": "Large Dual Encoders Are Generalizable Retrievers"
|
| 261 |
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| 615 |
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| 617 |
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| 619 |
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| 620 |
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"title": "Why Does ChatGPT Fall Short in Providing Truthful Answers?"
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| 622 |
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| 623 |
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| 624 |
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"title": "RETA-LLM: A Retrieval-Augmented Large Language Model Toolkit"
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| 625 |
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| 626 |
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| 627 |
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| 628 |
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| 629 |
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| 630 |
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| 631 |
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| 632 |
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"title": "Revisiting Challenges in Data-to-Text Generation with Fact Grounding"
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| 633 |
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| 634 |
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| 635 |
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| 636 |
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"title": "Automatic Evaluation of Attribution by Large Language Models"
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| 637 |
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| 638 |
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| 639 |
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| 640 |
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"title": "Context-faithful Prompting for Large Language Models"
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| 641 |
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| 642 |
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| 643 |
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| 644 |
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| 645 |
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| 646 |
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| 647 |
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| 648 |
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| 649 |
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| 650 |
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| 651 |
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| 652 |
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"title": "Looking for a Needle in a Haystack: A Comprehensive Study of Hallucinations in Neural Machine Translation"
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| 653 |
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| 654 |
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| 655 |
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| 656 |
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"title": "Fixing Model Bugs with Natural Language Patches"
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| 657 |
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| 658 |
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| 659 |
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| 660 |
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"title": "PURR: Efficiently Editing Language Model Hallucinations by Denoising Language Model Corruptions"
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| 661 |
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| 662 |
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| 663 |
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| 664 |
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"title": "Instruction Mining: High-Quality Instruction Data Selection for Large Language Models"
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| 665 |
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| 666 |
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| 667 |
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| 668 |
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"title": "Augmented Large Language Models with Parametric Knowledge Guiding"
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| 669 |
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| 670 |
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| 671 |
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| 672 |
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| 673 |
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| 674 |
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| 675 |
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| 676 |
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"title": "Knowledge Solver: Teaching LLMs to Search for Domain Knowledge from Knowledge Graphs"
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| 677 |
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| 678 |
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| 679 |
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| 680 |
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"title": "Mitigating Language Model Hallucination with Interactive Question-Knowledge Alignment"
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| 681 |
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| 682 |
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| 683 |
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| 684 |
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"title": "Summarization is (Almost) Dead"
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| 685 |
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| 686 |
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| 687 |
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"arxivId": "2205.11482",
|
| 688 |
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"title": "Tracing Knowledge in Language Models Back to the Training Data"
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| 689 |
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| 690 |
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|
| 691 |
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|
| 692 |
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|
| 693 |
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|
| 694 |
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|
| 695 |
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| 696 |
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"title": "Knowledge Enhanced Fine-Tuning for Better Handling Unseen Entities in Dialogue Generation"
|
| 697 |
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| 698 |
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|
| 699 |
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"arxivId": "2212.10711",
|
| 700 |
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"title": "Task Ambiguity in Humans and Language Models"
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| 701 |
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| 702 |
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|
| 703 |
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"arxivId": "2309.00240",
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| 704 |
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"title": "FactLLaMA: Optimizing Instruction-Following Language Models with External Knowledge for Automated Fact-Checking"
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| 705 |
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| 706 |
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| 707 |
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"arxivId": "2308.11764",
|
| 708 |
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"title": "Halo: Estimation and Reduction of Hallucinations in Open-Source Weak Large Language Models"
|
| 709 |
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| 710 |
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|
| 711 |
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"arxivId": "2309.05936",
|
| 712 |
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"title": "Do PLMs Know and Understand Ontological Knowledge?"
|
| 713 |
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|
| 714 |
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|
| 715 |
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"arxivId": "2305.14623",
|
| 716 |
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"title": "Self-Checker: Plug-and-Play Modules for Fact-Checking with Large Language Models"
|
| 717 |
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|
| 718 |
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|
| 719 |
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"arxivId": "2211.06196",
|
| 720 |
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"title": "Improving Factual Consistency in Summarization with Compression-Based Post-Editing"
|
| 721 |
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| 722 |
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|
| 723 |
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"arxivId": "2309.11064",
|
| 724 |
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"title": "Exploring the Relationship between LLM Hallucinations and Prompt Linguistic Nuances: Readability, Formality, and Concreteness"
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| 725 |
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| 726 |
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| 727 |
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"arxivId": "2309.02654",
|
| 728 |
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"title": "Zero-Resource Hallucination Prevention for Large Language Models"
|
| 729 |
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|
| 730 |
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|
| 731 |
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|
| 732 |
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"title": "Automatic Calibration and Error Correction for Large Language Models via Pareto Optimal Self-Supervision"
|
| 733 |
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|
| 734 |
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|
| 735 |
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|
| 736 |
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"title": "BatGPT: A Bidirectional Autoregessive Talker from Generative Pre-trained Transformer"
|
| 737 |
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|
| 738 |
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|
| 739 |
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"arxivId": "2309.08594",
|
| 740 |
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"title": "\"Merge Conflicts!\" Exploring the Impacts of External Distractors to Parametric Knowledge Graphs"
|
| 741 |
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|
| 742 |
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|
| 743 |
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"arxivId": "2308.04215",
|
| 744 |
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"title": "Hybrid-RACA: Hybrid Retrieval-Augmented Composition Assistance for Real-time Text Prediction"
|
| 745 |
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}
|
| 746 |
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}
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