---
title: "Who cites David Bau — 1,142 papers of our library"
description: "1,142 papers of the Elamur Research library cite the works of David Bau, found in their bibliographies · page 1 of 3 · 1–100; the most cited first."
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# Who cites David Bau

1,328 citations of their works from 1,142 papers of our library (of the 149,439 whose bibliographies we read) · 248 with a page here, the most cited first · page 1 of 3 · 1–100 · [← David Bau](https://md.research.elamur.ai/free/en/authors/david-bau/)

**1** [2](https://md.research.elamur.ai/free/en/authors/david-bau/cited-by/page/2/) [3](https://md.research.elamur.ai/free/en/authors/david-bau/cited-by/page/3/) [next →](https://md.research.elamur.ai/free/en/authors/david-bau/cited-by/page/2/)

1. **[Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://md.research.elamur.ai/free/en/papers/selvaraju-2016-grad-cam-visual-explanations-deep/) [International Journal of Computer Vision](https://md.research.elamur.ai/free/en/venues/international-journal-of-computer-vision/)**Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, et al.*2016 · 6,394 citations*
2. **[A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://md.research.elamur.ai/free/en/papers/huang-2023-survey-hallucination-large-language-models/) [ACM Transactions on Information Systems](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-information-systems/)**Lei Huang, Weijiang Yu, Weitao Ma, et al.*2023 · 3,231 citations*
3. **[A Survey of Large Language Models](https://md.research.elamur.ai/free/en/papers/zhao-2023-survey-large-language-models/) [Frontiers of Computer Science](https://md.research.elamur.ai/free/en/venues/frontiers-of-computer-science/)**Wayne Xin Zhao, Kun Zhou, Junyi Li, et al.*2023 · 2,875 citations*
4. **[Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2023-siren-s-song-ai-ocean/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Yue Zhang, Yafu Li, Leyang Cui, et al.*2023 · 977 citations*
5. **[GPT-NeoX-20B: An Open-Source Autoregressive Language Model](https://md.research.elamur.ai/free/en/papers/black-2022-gpt-neox-20b-open-source/) [BigScience Episode – Workshop on Challenges & Perspectives in Creating Large Language Models](https://md.research.elamur.ai/free/en/venues/bigscience/)**Sidney Black, Stella Biderman, Eric Hallahan, et al.*2022 · 831 citations*
6. **[Explainability for Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/zhao-2023-explainability-large-language-models-survey/) [ACM Transactions on Intelligent Systems and Technology](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-intelligent-systems-and-technology/)**Haiyan Zhao, Hanjie Chen, Fan Yang, et al.*2023 · 790 citations*
7. **[Inference-Time Intervention: Eliciting Truthful Answers from a Language Model](https://md.research.elamur.ai/free/en/papers/li-2023-inference-time-intervention-eliciting-truthful/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Kenneth Li, Oam Patel, Fernanda Viegas, et al.*2023 · 492 citations*
8. [**Representation Engineering: A Top-Down Approach to AI Transparency arXiv:2310.01405**Andy Zou, Long Phan, Sarah Chen, et al.*2023 · 397 citations*](https://md.research.elamur.ai/free/en/papers/zou-2023-representation-engineering-top-down-approach/)
9. **[Transformer Feed-Forward Layers Build Predictions by Promoting Concepts in the Vocabulary Space](https://md.research.elamur.ai/free/en/papers/geva-2022-transformer-feed-forward-layers-build/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Mor Geva, Avi Caciularu, Kevin Ro Wang, et al.*2022 · 346 citations*
10. [**Challenges and Applications of Large Language Models arXiv:2307.10169**Jean Kaddour, Joshua Harris, Maximilian Mozes, et al.*2023 · 301 citations*](https://md.research.elamur.ai/free/en/papers/kaddour-2023-challenges-applications-large-language-models/)
11. [**DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models arXiv:2309.03883**Yung-Sung Chuang, Yujia Xie, Hongyin Luo, et al.*2023 · 269 citations*](https://md.research.elamur.ai/free/en/papers/chuang-2023-dola-decoding-contrasting-layers-improves/)
12. **[HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models](https://md.research.elamur.ai/free/en/papers/gutierrez-2024-hipporag-neurobiologically-inspired-long-term/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Bernal Jimenez Gutierrez, Yiheng Shu, Yu Gu, et al.*2024 · 268 citations*
13. [**TrustLLM: Trustworthiness in Large Language Models arXiv:2401.05561**Yue Huang, Lichao Sun, Haoran Wang, et al.*2024 · 237 citations*](https://md.research.elamur.ai/free/en/papers/huang-2024-trustllm-trustworthiness-large-language-models/)
14. [**Steering Language Models With Activation Engineering arXiv:2308.10248**Alexander Matt Turner, Lisa Thiergart, Gavin Leech, et al.*2023 · 233 citations*](https://md.research.elamur.ai/free/en/papers/turner-2023-steering-language-models-activation-engineering/)
15. **[Combating Misinformation in the Age of LLMs: Opportunities and Challenges](https://md.research.elamur.ai/free/en/papers/chen-2023-combating-misinformation-age-llms-opportunities/) [AI Magazine](https://md.research.elamur.ai/free/en/venues/ai-magazine/)**Canyu Chen, Kai Shu*2023 · 214 citations*
16. **[Knowledge Conflicts for LLMs: A Survey](https://md.research.elamur.ai/free/en/papers/xu-2024-knowledge-conflicts-llms-survey/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Rongwu Xu, Zehan Qi, Zhijiang Guo, et al.*2024 · 182 citations*
17. **[Do Llamas Work in English? On the Latent Language of Multilingual Transformers](https://md.research.elamur.ai/free/en/papers/wendler-2024-do-llamas-work-english-latent/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Chris Wendler, Veniamin Veselovsky, Giovanni Monea, et al.*2024 · 177 citations*
18. [**AI Alignment: A Comprehensive Survey arXiv:2310.19852**Jiaming Ji, Tianyi Qiu, Boyuan Chen, et al.*2023 · 173 citations*](https://md.research.elamur.ai/free/en/papers/ji-2023-ai-alignment-comprehensive-survey/)
19. [**Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision arXiv:2312.09390**Collin Burns, Pavel Izmailov, Jan Hendrik Kirchner, et al.*2023 · 172 citations*](https://md.research.elamur.ai/free/en/papers/burns-2023-weak-strong-generalization-eliciting-strong/)
20. [**Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity arXiv:2310.07521**Cunxiang Wang, Xiaoze Liu, Yuanhao Yue, et al.*2023 · 171 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-survey-factuality-large-language-models/)
21. [**The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasets arXiv:2310.06824**Samuel Marks, Max Tegmark*2023 · 169 citations*](https://md.research.elamur.ai/free/en/papers/marks-2023-geometry-truth-emergent-linear-structure/)
22. [**Adaptive Chameleon or Stubborn Sloth: Revealing the Behavior of Large Language Models in Knowledge Conflicts arXiv:2305.13300**Jian Xie, Kai Zhang, Jiangjie Chen, et al.*2023 · 168 citations*](https://md.research.elamur.ai/free/en/papers/xie-2023-adaptive-chameleon-stubborn-sloth-revealing/)
23. **[Factuality Enhanced Language Models for Open-Ended Text Generation](https://md.research.elamur.ai/free/en/papers/lee-2022-factuality-enhanced-language-models-open/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Nayeon Lee, Wei Ping, Peng Xu, et al.*2022 · 168 citations*
24. **[The Linear Representation Hypothesis and the Geometry of Large Language Models](https://md.research.elamur.ai/free/en/papers/park-2023-linear-representation-hypothesis-geometry-large/) [International Conference on Machine Learning (ICML)](https://md.research.elamur.ai/free/en/venues/international-conference-on-machine-learning/)**Kiho Park, Yo Joong Choe, Victor Veitch*2023 · 165 citations*
25. **[Language Models as Agent Models](https://md.research.elamur.ai/free/en/papers/andreas-2022-language-models-as-agent-models/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jacob Andreas*2022 · 165 citations*
26. **[In-Context Learning Creates Task Vectors](https://md.research.elamur.ai/free/en/papers/hendel-2023-context-learning-creates-task-vectors/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Roee Hendel, Mor Geva, Amir Globerson*2023 · 159 citations*
27. **[Large Language Models with Controllable Working Memory](https://md.research.elamur.ai/free/en/papers/li-2023-large-language-models-controllable-working/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Daliang Li, Ankit Singh Rawat, Manzil Zaheer, et al.*2023 · 158 citations*
28. **[How does GPT-2 compute greater-than?: Interpreting mathematical abilities in a pre-trained language model](https://md.research.elamur.ai/free/en/papers/hanna-2023-gpt-2-compute-greater-than/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Michael Hanna, Ollie Liu, Alexandre Variengien*2023 · 151 citations*
29. [**A Review on Language Models as Knowledge Bases arXiv:2204.06031**Badr AlKhamissi, Millicent Li, Asli Celikyilmaz, et al.*2022 · 150 citations*](https://md.research.elamur.ai/free/en/papers/alkhamissi-2022-review-language-models-knowledge-bases/)
30. **[Context-faithful Prompting for Large Language Models](https://md.research.elamur.ai/free/en/papers/zhou-2023-context-faithful-prompting-large-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Wenxuan Zhou, Sheng Zhang, Hoifung Poon, et al.*2023 · 148 citations*
31. [**TOFU: A Task of Fictitious Unlearning for LLMs arXiv:2401.06121**Pratyush Maini, Zhili Feng, Avi Schwarzschild, et al.*2024 · 144 citations*](https://md.research.elamur.ai/free/en/papers/maini-2024-tofu-task-fictitious-unlearning-llms/)
32. **[PMET: Precise Model Editing in a Transformer](https://md.research.elamur.ai/free/en/papers/li-2023-pmet-precise-model-editing-transformer/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Xiaopeng Li; Shasha Li; Shezheng Song; Jing Yang; Jun Ma;, Jie Yu*2023 · 135 citations*
33. [**The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearning arXiv:2403.03218**Nathaniel Li, Alexander Pan, Anjali Gopal, et al.*2024 · 127 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-wmdp-benchmark-measuring-reducing-malicious/)
34. [**Function Vectors in Large Language Models arXiv:2310.15213**Eric Todd, Millicent L. Li, Arnab Sen Sharma, et al.*2023 · 127 citations*](https://md.research.elamur.ai/free/en/papers/todd-2023-function-vectors-large-language-models/)
35. [**Negative Preference Optimization: From Catastrophic Collapse to Effective Unlearning arXiv:2404.05868**Ruiqi Zhang, Licong Lin, Yu Bai, et al.*2024 · 126 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2024-negative-preference-optimization-catastrophic-collapse/)
36. **[LM vs LM: Detecting Factual Errors via Cross Examination](https://md.research.elamur.ai/free/en/papers/cohen-2023-lm-vs-lm-detecting-factual/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Roi Cohen, May Hamri, Mor Geva, et al.*2023 · 126 citations*
37. [**Large Language Model Alignment: A Survey arXiv:2309.15025**Tianhao Shen, Renren Jin, Yufei Huang, et al.*2023 · 124 citations*](https://md.research.elamur.ai/free/en/papers/shen-2023-large-language-model-alignment-survey/)
38. [**Finding Neurons in a Haystack: Case Studies with Sparse Probing arXiv:2305.01610**Wes Gurnee, Neel Nanda, Matthew Pauly, et al.*2023 · 123 citations*](https://md.research.elamur.ai/free/en/papers/gurnee-2023-finding-neurons-haystack-case-studies/)
39. [**Transformer-Patcher: One Mistake worth One Neuron arXiv:2301.09785**Zeyu Huang, Yikang Shen, Xiaofeng Zhang, et al.*2023 · 123 citations*](https://md.research.elamur.ai/free/en/papers/huang-2023-transformer-patcher-one-mistake-worth/)
40. [**Quiet-STaR: Language models can teach themselves to think before speaking***2024 · 122 citations*](https://md.research.elamur.ai/free/en/papers/al-2024-quiet-star-language-models-teach/)
41. [**The Alignment Problem from a Deep Learning Perspective arXiv:2209.00626**Richard Ngo, Lawrence Chan, Soren Mindermann*2022 · 121 citations*](https://md.research.elamur.ai/free/en/papers/ngo-2022-alignment-problem-deep-learning-perspective/)
42. [**Identifying and Controlling Important Neurons in Neural Machine Translation arXiv:1811.01157**Anthony Bau, Yonatan Belinkov, Hassan Sajjad, et al.*2018 · 121 citations*](https://md.research.elamur.ai/free/en/papers/bau-2018-identifying-controlling-important-neurons-neural/)
43. [**Emergent Linear Representations in World Models of Self-Supervised Sequence Models arXiv:2309.00941**Neel Nanda, Andrew Lee, Martin Wattenberg*2023 · 116 citations*](https://md.research.elamur.ai/free/en/papers/nanda-2023-emergent-linear-representations-world-models/)
44. [**Physics of Language Models: Part 3.1, Knowledge Storage and Extraction arXiv:2309.14316**Zeyuan Allen-Zhu, Yuanzhi Li*2023 · 116 citations*](https://md.research.elamur.ai/free/en/papers/allen-zhu-2023-physics-language-models-part-3/)
45. [**Inspecting and Editing Knowledge Representations in Language Models arXiv:2304.00740**Evan Hernandez, Belinda Z. Li, Jacob Andreas*2023 · 115 citations*](https://md.research.elamur.ai/free/en/papers/hernandez-2023-inspecting-editing-knowledge-representations-language/)
46. **[Calibrating Factual Knowledge in Pretrained Language Models](https://md.research.elamur.ai/free/en/papers/dong-2022-calibrating-factual-knowledge-pretrained-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Qingxiu Dong, Damai Dai, Yifan Song, et al.*2022 · 115 citations*
47. **[MoEfication: Transformer Feed-forward Layers are Mixtures of Experts](https://md.research.elamur.ai/free/en/papers/zhang-2022-moefication-transformer-feed-forward-layers/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhengyan Zhang, Yankai Lin, Zhiyuan Liu, et al.*2022 · 115 citations*
48. [**Mechanistic Interpretability for AI Safety — A Review arXiv:2404.14082**Leonard Bereska, Efstratios Gavves*2024 · 108 citations*](https://md.research.elamur.ai/free/en/papers/bereska-2024-mechanistic-interpretability-ai-safety-review/)
49. [**Studying Large Language Model Generalization with Influence Functions arXiv:2308.03296**Roger Grosse, Juhan Bae, Cem Anil, et al.*2023 · 105 citations*](https://md.research.elamur.ai/free/en/papers/grosse-2023-studying-large-language-model-generalization/)
50. **[How do Large Language Models Handle Multilingualism?](https://md.research.elamur.ai/free/en/papers/zhao-2024-large-language-models-handle-multilingualism/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yiran Zhao, Wenxuan Zhang, Guizhen Chen, et al.*2024 · 102 citations*
51. **[Inseq: An Interpretability Toolkit for Sequence Generation Models](https://md.research.elamur.ai/free/en/papers/sarti-2023-inseq-interpretability-toolkit-sequence-generation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Gabriele Sarti, Nils Feldhus, Ludwig Sickert, et al.*2023 · 97 citations*
52. [**Towards Best Practices of Activation Patching in Language Models: Metrics and Methods arXiv:2309.16042**Fred Zhang, Neel Nanda*2023 · 97 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-towards-best-practices-activation-patching/)
53. [**Dataset Distillation arXiv:1811.10959**Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, et al.*2018 · 97 citations*](https://md.research.elamur.ai/free/en/papers/wang-2018-dataset-distillation/)
54. **[Do Llamas Work in English? On the Latent Language of Multilingual Transformers](https://md.research.elamur.ai/free/en/papers/wendler-2024-llamas-work-english-latent-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Chris Wendler, Veniamin Veselovsky, Giovanni Monea, et al.*2024 · 96 citations*
55. [**LLM Inference Unveiled: Survey and Roofline Model Insights arXiv 2024**Zhihang Yuan, Yuzhang Shang, Yang Zhou, et al.*2024 · 91 citations*](https://md.research.elamur.ai/free/en/papers/yuan-2024-llm-inference-unveiled-survey-roofline/)
56. [**Better & Faster Large Language Models via Multi-token Prediction arXiv:2404.19737**Fabian Gloeckle, Badr Youbi Idrissi, Baptiste Roziere, et al.*2024 · 90 citations*](https://md.research.elamur.ai/free/en/papers/gloeckle-2024-better-faster-large-language-models/)
57. **[Cross-Lingual Consistency of Factual Knowledge in Multilingual Language Models](https://md.research.elamur.ai/free/en/papers/qi-2023-cross-lingual-consistency-factual-knowledge/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jirui Qi, Raquel Fernández, Arianna Bisazza*2023 · 90 citations*
58. **[Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Survey](https://md.research.elamur.ai/free/en/papers/kumar-2023-language-generation-models-can-cause/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Sachin Kumar, Vidhisha Balachandran, Lucille Njoo, et al.*2023 · 90 citations*
59. [**Foundational Challenges in Assuring Alignment and Safety of Large Language Models arXiv:2404.09932**Usman Anwar, Abulhair Saparov, Javier Rando, et al.*2024 · 87 citations*](https://md.research.elamur.ai/free/en/papers/anwar-2024-foundational-challenges-assuring-alignment-safety/)
60. [**Rethinking Interpretability in the Era of Large Language Models arXiv:2402.01761**Chandan Singh, Jeevana Priya Inala, Michel Galley, et al.*2024 · 87 citations*](https://md.research.elamur.ai/free/en/papers/singh-2024-rethinking-interpretability-era-large-language/)
61. **[Retrieval-Augmented Generation for Natural Language Processing: A Survey](https://md.research.elamur.ai/free/en/papers/wu-2024-retrieval-augmented-generation-natural-language/) [Artificial Intelligence Review](https://md.research.elamur.ai/free/en/venues/artificial-intelligence-review/)**Shangyu Wu, Ying Xiong, Yufei Cui, et al.*2024 · 86 citations*
62. **[DisentQA: Disentangling Parametric and Contextual Knowledge with Counterfactual Question Answering](https://md.research.elamur.ai/free/en/papers/neeman-2023-disentqa-disentangling-parametric-contextual-knowledge/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ella Neeman, Roee Aharoni, Or Honovich, et al.*2023 · 85 citations*
63. **[Finding Skill Neurons in Pre-trained Transformer-based Language Models](https://md.research.elamur.ai/free/en/papers/wang-2022-finding-skill-neurons-pre-trained/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Xiaozhi Wang, Kaiyue Wen, Zhengyan Zhang, et al.*2022 · 85 citations*
64. **[Detoxifying Large Language Models via Knowledge Editing](https://md.research.elamur.ai/free/en/papers/wang-2024-detoxifying-large-language-models-knowledge/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mengru Wang, Ningyu Zhang, Ziwen Xu, et al.*2024 · 84 citations*
65. [**Patchscopes: A Unifying Framework for Inspecting Hidden Representations of Language Models arXiv:2401.06102**Asma Ghandeharioun, Avi Caciularu, Adam Pearce, et al.*2024 · 84 citations*](https://md.research.elamur.ai/free/en/papers/ghandeharioun-2024-patchscopes-unifying-framework-inspecting-hidden/)
66. **[Improving Alignment and Robustness with Circuit Breakers](https://md.research.elamur.ai/free/en/papers/zou-2024-improving-alignment-robustness-circuit-breakers/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Andy Zou, Long Phan, Justin Wang, et al.*2024 · 83 citations*
67. [**Linearity of Relation Decoding in Transformer Language Models arXiv:2308.09124**Evan Hernandez, Arnab Sen Sharma, Tal Haklay, et al.*2023 · 82 citations*](https://md.research.elamur.ai/free/en/papers/hernandez-2023-linearity-relation-decoding-transformer-language/)
68. [**Finding Alignments Between Interpretable Causal Variables and Distributed Neural Representations arXiv:2303.02536**Atticus Geiger, Zhengxuan Wu, Christopher Potts, et al.*2023 · 81 citations*](https://md.research.elamur.ai/free/en/papers/geiger-2023-finding-alignments-between-interpretable-causal/)
69. **[Interpretability at Scale: Identifying Causal Mechanisms in Alpaca](https://md.research.elamur.ai/free/en/papers/wu-2023-interpretability-scale-identifying-causal-mechanisms/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Zhengxuan Wu, Atticus Geiger, Thomas Icard, et al.*2023 · 81 citations*
70. **[Factuality of Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/wang-2024-factuality-large-language-models-survey/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yuxia Wang, Minghan Wang, Muhammad Arslan Manzoor, et al.*2024 · 77 citations*
71. [**Localizing Model Behavior with Path Patching arXiv:2304.05969**Nicholas Goldowsky-Dill, Chris MacLeod, Lucas Sato, et al.*2023 · 76 citations*](https://md.research.elamur.ai/free/en/papers/goldowsky-dill-2023-localizing-model-behavior-path-patching/)
72. [**A Primer on the Inner Workings of Transformer-based Language Models arXiv:2405.00208**Javier Ferrando, Gabriele Sarti, Arianna Bisazza, et al.*2024 · 72 citations*](https://md.research.elamur.ai/free/en/papers/ferrando-2024-primer-inner-workings-transformer-based/)
73. [**The Unreasonable Ineffectiveness of the Deeper Layers arXiv:2403.17887**Andrey Gromov, Kushal Tirumala, Hassan Shapourian, et al.*2024 · 71 citations*](https://md.research.elamur.ai/free/en/papers/gromov-2024-unreasonable-ineffectiveness-deeper-layers/)
74. **[Entity Cloze By Date: What LMs Know About Unseen Entities](https://md.research.elamur.ai/free/en/papers/onoe-2022-entity-cloze-date-what-lms/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yasumasa Onoe, Michael Zhang, Eunsol Choi, et al.*2022 · 71 citations*
75. [**LLMs Know More Than They Show: On the Intrinsic Representation of LLM Hallucinations arXiv:2410.02707**Hadas Orgad, Michael Toker, Zorik Gekhman, et al.*2024 · 68 citations*](https://md.research.elamur.ai/free/en/papers/orgad-2024-llms-know-more-than-they/)
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77. [**Learning by Distilling Context arXiv:2209.15189**Charlie Snell, Dan Klein, Ruiqi Zhong*2022 · 67 citations*](https://md.research.elamur.ai/free/en/papers/snell-2022-learning-distilling-context/)
78. [**Assessing the Brittleness of Safety Alignment via Pruning and Low-Rank Modifications arXiv:2402.05162**Boyi Wei, Kaixuan Huang, Yangsibo Huang, et al.*2024 · 66 citations*](https://md.research.elamur.ai/free/en/papers/wei-2024-assessing-brittleness-safety-alignment-pruning/)
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80. [**A Comprehensive Survey on Long Context Language Modeling arXiv:2503.17407**Jiaheng Liu, Dawei Zhu, Zhiqi Bai, et al.*2025 · 64 citations*](https://md.research.elamur.ai/free/en/papers/liu-2025-comprehensive-survey-long-context-language/)
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82. [**Linear Representations of Sentiment in Large Language Models arXiv:2310.15154**Curt Tigges, Oskar John Hollinsworth, Atticus Geiger, et al.*2023 · 64 citations*](https://md.research.elamur.ai/free/en/papers/tigges-2023-linear-representations-sentiment-large-language/)
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84. [**Does Circuit Analysis Interpretability Scale? Evidence from Multiple Choice Capabilities in Chinchilla arXiv:2307.09458**Tom Lieberum, Matthew Rahtz, Janos Kramar, et al.*2023 · 58 citations*](https://md.research.elamur.ai/free/en/papers/lieberum-2023-circuit-analysis-interpretability-scale-evidence/)
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86. **[Hopping Too Late: Exploring the Limitations of Large Language Models on Multi-Hop Queries](https://md.research.elamur.ai/free/en/papers/biran-2024-hopping-too-late-exploring-limitations/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Eden Biran, Daniela Gottesman, Sohee Yang, et al.*2024 · 55 citations*
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93. **[Learning to Edit: Aligning LLMs with Knowledge Editing](https://md.research.elamur.ai/free/en/papers/jiang-2024-learning-edit-aligning-llms-knowledge/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuxin Jiang, Yufei Wang, Chuhan Wu, et al.*2024 · 39 citations*
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95. **[Exploring Concept Depth: How Large Language Models Acquire Knowledge and Concept at Different Layers?](https://md.research.elamur.ai/free/en/papers/jin-2025-exploring-concept-depth-how-large/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Mingyu Jin, Qinkai Yu, Jingyuan Huang, et al.*2025 · 36 citations*
96. [**Chinese SimpleQA: A Chinese Factuality Evaluation for Large Language Models arXiv:2411.07140**Yancheng He, Shilong Li, Jiaheng Liu, et al.*2024 · 34 citations*](https://md.research.elamur.ai/free/en/papers/he-2024-chinese-simpleqa-chinese-factuality/)
97. **[Retrieval-Augmented Multilingual Knowledge Editing](https://md.research.elamur.ai/free/en/papers/wang-2024-retrieval-augmented-multilingual-knowledge-editing/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Weixuan Wang, Barry Haddow, Alexandra Birch*2024 · 34 citations*
98. **[The Hidden Attention of Mamba Models](https://md.research.elamur.ai/free/en/papers/ali-2025-hidden-attention-mamba-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ameen Ali Ali, Itamar Zimerman, Lior Wolf*2025 · 32 citations*
99. **[Lost in Multilinguality: Dissecting Cross-lingual Factual Inconsistency in Transformer Language Models](https://md.research.elamur.ai/free/en/papers/wang-2025-lost-multilinguality-dissecting-cross-lingual/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mingyang Wang, Heike Adel, Lukas Lange, et al.*2025 · 31 citations*
100. [**The Life Cycle of Knowledge in Big Language Models: A Survey arXiv:2303.07616 · Machine Intelligence Research. vol. 21, no. 2, pp. 217-238, 2024**Boxi Cao, Hongyu Lin, Xianpei Han, et al.*2023 · 31 citations*](https://md.research.elamur.ai/free/en/papers/cao-2023-life-cycle-knowledge-big-language/)

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