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Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge.
Linear algebraic structure of word senses, with applications to polysemy
Arora, S., Li, Y., Liang, Y., Ma, T., and Risteski, A · 2018
Earlier work this paper cites.
Natural questions: a benchmark for question answering research
Kwiatkowski, T., Palomaki, J., Redfield, O., Collins, M., Parikh, A., Alberti, C., Epstein, D., Polosukhin, I., Devlin, J., Lee, K., et al · 2019
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Thread: circuits
Cammarata, N., Carter, S., Goh, G., Olah, C., Petrov, M., Schubert, L., Voss, C., Egan, B., and Lim, S. K · 2020
Earlier work this paper cites.
How much knowledge can you pack into the parameters of a language model?
Roberts, A., Raffel, C., and Shazeer, N · 2020
Earlier work this paper cites.
A mathematical framework for transformer circuits
Elhage, N., Nanda, N., Olsson, C., Henighan, T., Joseph, N., Mann, B., Askell, A., Bai, Y., Chen, A., Conerly, T., et al · 2021
Earlier work this paper cites.
Transformer feed-forward layers are key-value memories
Geva, M., Schuster, R., Berant, J., and Levy, O · 2021
Earlier work this paper cites.
Entity-based knowledge conflicts in question answering
Longpre, S., Perisetla, K., Chen, A., Ramesh, N., DuBois, C., and Singh, S · 2021
Earlier work this paper cites.
Rich knowledge sources bring complex knowledge conflicts: Recalibrating models to reflect conflicting evidence
Chen, H.-T., Zhang, M., and Choi, E · 2022
Earlier work this paper cites.
Elhage, N., Hume, T., Olsson, C., Schiefer, N., Henighan, T., Kravec, S., Hatfield-Dodds, Z., Lasenby, R., Drain, D., Chen, C., et al · 2022
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In-context learning and induction heads
Olsson, C., Elhage, N., Nanda, N., Joseph, N., DasSarma, N., Henighan, T., Mann, B., Askell, A., Bai, Y., Chen, A., et al · 2022
Earlier work this paper cites.
Transformers learn to implement preconditioned gradient descent for in-context learning
Ahn, K., Cheng, X., Daneshmand, H., and Sra, S · 2023
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Scaling laws for associative memories
Cabannes, V., Dohmatob, E., and Bietti, A · 2023
Earlier work this paper cites.
Retrieval-augmented generation for large language models: A survey
Gao, Y., Xiong, Y., Gao, X., Jia, K., Pan, J., Bi, Y., Dai, Y., Sun, J., and Wang, H · 2023
Earlier work this paper cites.
A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions
Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al · 2023
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Phi-2: The surprising power of small language models
Javaheripi, M., Bubeck, S., Abdin, M., Aneja, J., Bubeck, S., Mendes, C. C. T., Chen, W., Del Giorno, A., Eldan, R., Gopi, S., et al · 2023
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The memotrap dataset, 2023
Liu, A. and Liu, J · 2023
Earlier work this paper cites.
Copy suppression: Comprehensively understanding an attention head
McDougall, C., Conmy, A., Rushing, C., McGrath, T., and Nanda, N · 2023
Earlier work this paper cites.
Fact finding: Attempting to reverse-engineer factual recall on the neuron level
Nanda, N., Rajamanoharan, S., Kramár, J., and Shah, R · 2023
Cited alongside, same era.
” merge conflicts!” exploring the impacts of external distractors to parametric knowledge graphs
Qian, C., Zhao, X., and Wu, S. T · 2023
Cited alongside, same era.
Large language models can be easily distracted by irrelevant context
Shi, F., Chen, X., Misra, K., Scales, N., Dohan, D., Chi, E. H., Schärli, N., and Zhou, D · 2023
Cited alongside, same era.
Llama 2: Open foundation and fine-tuned chat models
Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al · 2023
Cited alongside, same era.
Characterizing mechanisms for factual recall in language models
Yu, Q., Merullo, J., and Pavlick, E · 2023
Cited alongside, same era.
Interpreting key mechanisms of factual recall in transformer-based language models
Lv, A., Chen, Y., Zhang, K., Wang, Y., Liu, L., Wen, J.-R., Xie, J., and Yan, R · 2024
Later among the works it cites.
Understanding factual recall in transformers via associative memories
Nichani, E., Lee, J. D., and Bietti, A · 2024
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A mechanistic explanatory strategy for xai
Rabiza, M · 2024
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Ircan: Mitigating knowledge conflicts in llm generation via identifying and reweighting context-aware neurons
Shi, D., Jin, R., Shen, T., Dong, W., Wu, X., and Xiong, D · 2024
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Trusting your evidence: Hallucinate less with context-aware decoding
Shi, W., Han, X., Lewis, M., Tsvetkov, Y., Zettlemoyer, L., and Yih, W.-t · 2024
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Mitigating temporal misalignment by discarding outdated facts
Zhang, M. and Choi, E · 2023
Cited alongside, same era.
Context-faithful prompting for large language models
Zhou, W., Zhang, S., Poon, H., and Chen, M · 2023
Cited alongside, same era.
Stable lm 2 1.6 b technical report
Bellagente, M., Tow, J., Mahan, D., Phung, D., Zhuravinskyi, M., Adithyan, R., Baicoianu, J., Brooks, B., Cooper, N., Datta, A., et al · 2024
Cited alongside, same era.
Birth of a transformer: A memory viewpoint
Bietti, A., Cabannes, V., Bouchacourt, D., Jegou, H., and Bottou, L · 2024
Cited alongside, same era.
Language model behavior: A comprehensive survey
Chang, T. A. and Bergen, B. K · 2024
Cited alongside, same era.
Dubey, A., Jauhri, A., Pandey, A., Kadian, A., Al-Dahle, A., Letman, A., Mathur, A., Schelten, A., Yang, A., Fan, A., et al · 2024
Cited alongside, same era.
Getting sick after seeing a doctor? diagnosing and mitigating knowledge conflicts in event temporal reasoning
Fang, T., Wang, Z., Zhou, W., Zhang, H., Song, Y., and Chen, M · 2024
Cited alongside, same era.
Later among the works it cites.
Blinded by generated contexts: How language models merge generated and retrieved contexts when knowledge conflicts?
Tan, H., Sun, F., Yang, W., Wang, Y., Cao, Q., and Cheng, X · 2024
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Gemma: Open models based on gemini research and technology
Team, G., Mesnard, T., Hardin, C., Dadashi, R., Bhupatiraju, S., Pathak, S., Sifre, L., Rivière, M., Kale, M. S., Love, J., et al · 2024
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Knowledge editing for large language models: A survey
Wang, S., Zhu, Y., Liu, H., Zheng, Z., Chen, C., and Li, J · 2024
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Adaptive chameleon or stubborn sloth: Revealing the behavior of large language models in knowledge conflicts
Xie, J., Zhang, K., Chen, J., Lou, R., and Su, Y · 2024
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Knowledge conflicts for llms: A survey
Xu, R., Qi, Z., Guo, Z., Wang, C., Wang, H., Zhang, Y., and Xu, W · 2024
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Intuitive or dependent? investigating llms’ behavior style to conflicting prompts
Ying, J., Cao, Y., Xiong, K., Cui, L., He, Y., and Liu, Y · 2024
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Discerning and resolving knowledge conflicts through adaptive decoding with contextual information-entropy constraint
Yuan, X., Yang, Z., Wang, Y., Liu, S., Zhao, J., and Liu, K · 2024
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Trained transformers learn linear models in-context
Zhang, R., Frei, S., and Bartlett, P. L · 2024
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Massive values in self-attention modules are the key to contextual knowledge understanding
Jin, M., Mei, K., Xu, W., Sun, M., Tang, R., Du, M., Liu, Z., and Zhang, Y · 2025
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Tool learning with large language models: A survey
Qu, C., Dai, S., Wei, X., Cai, H., Wang, S., Yin, D., Xu, J., and Wen, J.-R · 2025
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The rise and potential of large language model based agents: A survey
Xi, Z., Chen, W., Guo, X., He, W., Ding, Y., Hong, B., Zhang, M., Wang, J., Jin, S., Zhou, E., et al · 2025
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