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The repository for the survey paper <<Survey on Large Language Models Factuality: Knowledge, Retrieval and Domain-Specificity>>
| Date | Stars |
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| 2026-07-31 | 339 |
| 2026-08-06 | 339 |
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# LLM-Factuality-Survey The repository for the survey paper "[**Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity**](https://arxiv.org/abs/2310.07521)"  <p align="center"> Cunxiang Wang<sup>1,7</sup>*, Xiaoze Liu<sup>2</sup>*, Yuanhao Yue<sup>3</sup>*, Qipeng Guo<sup>4</sup>, Xiangkun Hu<sup>4</sup>, Xiangru Tang<sup>5</sup>, Tianhang Zhang<sup>6</sup>, Cheng Jiayang<sup>7</sup>, Yunzhi Yao<sup>8</sup>, Wenyang Gao<sup>1,8</sup>, Xuming Hu<sup>9</sup>, Zehan Qi<sup>9</sup>, Yidong Wang<sup>1</sup>, Linyi Yang<sup>1</sup>, Jindong Wang<sup>10</sup>, Xing Xie<sup>10</sup>, Zheng Zhang<sup>4,11</sup> and Yue Zhang<sup>1</sup>. </p> <p align="center"> 1. School of Engineering, Westlake University; 2. Purdue University; 3. Fudan University; 4. Amazon AWS AI Lab; 5. Yale University; 6. Shanghai Jiao Tong University; 7. HKUST; 8. Zhejiang University; 9. Tsinghua University; 10. Microsoft Research; 11. NYU Shanghai;<br> (*: Equal Contribution; Correspondence to: Yue Zhang) </p>  **NOTE:** As real-time updates may not be feasible for the arXiv paper. For the most recent developments and modifications, please consult this repository. We greatly appreciate and welcome pull requests or issues to enhance the quality of this survey. All contributions will be list in the <a href="#acknowledgements">acknowledgements</a> section. # Paper List ## Analysis of Factuality ### Knowledge Storage 1. Language Models as Knowledge Bases?. _Petroni et al._ 2019. [[Paper](https://doi.org/10.18653/v1/D19-1250)] 1. Locating and Editing Factual Associations in GPT. _Meng et al._ 2022. [[Paper](https://openreview.net/forum?id=-h6WAS6eE4)] 1. Transformer Feed-Forward Layers Are Key-Value Memories. _Geva et al._ 2021. [[Paper](https://doi.org/10.18653/v1/2021.emnlp-main.446)] 1. Transformer Feed-Forward Layers Build Predictions by Promoting Concepts in the Vocabulary Space. _Geva et al._ 2022. [[Paper](https://aclanthology.org/2022.emnlp-main.3)] 1. Dissecting Recall of Factual Associations in Auto-Regressive Language Models. _Globerson et al._ 2023. [[Paper](https://arxiv.org/abs/2304.14767)] 1. Journey to the Center of the Knowledge Neurons: Discoveries of Language-Independent Knowledge Neurons and Degenerate Knowledge Neurons. _Chen et al._ 2023. [[Paper](https://arxiv.org/abs/2308.13198)] 1. A rigorous study of integrated gradients method and extensions to internal neuron attributions. _Lundstrom et al._ 2022. [[Paper](https://arxiv.org/abs/2202.11912)] ### Knowledge Awareness 1. CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing. _Gou et al._ 2023. [[Paper](https://arxiv.org/abs/2305.11738)] 1. Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation. _Ren et al._ 2023. [[Paper](https://arxiv.org/abs/2307.11019)] 1. Do Large Language Models Know What They Don't Know?. _Yin et al._ 2023. [[Paper](https://doi.org/10.18653/v1/2023.findings-acl.551)] 1. A Survey on In-context Learning. _Dong et al._ 2023. [[Paper](https://arxiv.org/abs/2301.00234)] 1. Language Models (Mostly) Know What They Know. _Kadavath et al._ 2022. [[Paper](https://arxiv.org/abs/2207.05221)] 1. The internal state of an llm knows when its lying. _Azaria et al._ 2023. [[Paper](https://arxiv.org/abs/2304.13734)] 1. DRIFT: Detecting Representational Inconsistencies for Factual Truthfulness. _Bhatnagar et al._ arXiv 2025. [[Paper](https://arxiv.org/abs/2601.14210)] ### Parametric Knowledge vs Retrieved Knowledge 1. Generate rather than retrieve: Large language models are strong context generators. _Yu et al._ 2023. [[Paper](https://arxiv.org/abs/2209.10063)] 1. Beyond Factuality: A Comprehensive Evaluation of Large Language Models as Knowledge Generators. _Chen et al._ 2023. [[Paper](https://arxiv.org/abs/2310.07289)] 1. Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering. _Izacard et al.
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:4138bd67379572d7, llm:Repository description: 'The repository for the survey paper <<Survey on Large Language Models Factuality: Knowledge, Retrieval and Domain-Specificity>>'
matched fp:4138bd67379572d7, llm:Repository description: 'The repository for the survey paper <<Survey on Large Language Models Factuality: Knowledge, Retrieval and Domain-Specificity>>'
matched fp:4138bd67379572d7, llm:Repository description: 'The repository for the survey paper <<Survey on Large Language Models Factuality: Knowledge, Retrieval and Domain-Specificity>>'