Built on

Joshi, M., Choi, E., Weld, D.S., Zettlemoyer, L.: Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension. arXiv preprint arXiv:1705.03551 (2017)
Original
2017
Earlier work this paper cites.
Ott, M., Auli, M., Grangier, D., Ranzato, M.: Analyzing uncertainty in neural machine translation. In: International Conference on Machine Learning. pp. 3956–3965. PMLR (2018)
2018
Earlier work this paper cites.
Radford, A., Narasimhan, K., Salimans, T., Sutskever, I., et al.: Improving language understanding by generative pre-training (2018)
2018
Earlier work this paper cites.
Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2-7, 2019, Volume 1 (Long and Short Papers). pp. 4171–4186. Association for Computational Linguistics (2019). https://doi.org/10.18653/V1/N19-1423
2019
Earlier work this paper cites.
Petroni, F., Rocktäschel, T., Lewis, P., Bakhtin, A., Wu, Y., Miller, A.H., Riedel, S.: Language models as knowledge bases? arXiv preprint arXiv:1909.01066 (2019)
Original
2019
Earlier work this paper cites.
Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in Neural Information Processing Systems 33
2020
Earlier work this paper cites.
Desai, S., Durrett, G.: Calibration of pre-trained transformers. arXiv preprint arXiv:2003.07892 (2020)
Original
2020
Earlier work this paper cites.
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., Liu, P.J.: Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Machine Learning Research 21
2020
Earlier work this paper cites.
Roberts, A., Raffel, C., Shazeer, N.: How much knowledge can you pack into the parameters of a language model? In: Webber, B., Cohn, T., He, Y., Liu, Y. (eds.) Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, Online, November 16-20, 2020. pp. 5418–5426. Association for Computational Linguistics (2020). https://doi.org/10.18653/V1/2020.EMNLP-MAIN.437
2020
Earlier work this paper cites.

Similar

Glushkova, T., Zerva, C., Rei, R., Martins, A.F.: Uncertainty-aware machine translation evaluation. arXiv preprint arXiv:2109.06352 (2021)
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Jiang, Z., Araki, J., Ding, H., Neubig, G.: How can we know When language models know? on the calibration of language models for question answering. Trans. Assoc. Comput. Linguistics 9
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2022
Cited alongside, same era.

Then

Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24
2023
Later among the works it cites.
Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023)
Original
2023
Later among the works it cites.
Kuhn, L., Gal, Y., Farquhar, S.: Semantic uncertainty: Linguistic invariances for uncertainty estimation in natural language generation. In: The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenReview.net (2023)
2023
Later among the works it cites.
Ranaldi, L., Pucci, G.: When large language models contradict humans? large language models’ sycophantic behaviour. arXiv preprint arXiv:2311.09410 (2023)
Original

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2024

Understanding Knowledge Drift in LLMs through Misinformation

Fastowski, Alina, Kasneci, Gjergji

Understand

Large Language Models (LLMs) have revolutionized numerous applications, making them an integral part of our digital ecosystem.

However, their reliability becomes critical, especially when these models are exposed to misinformation.
We primarily analyze the susceptibility of state-of-the-art LLMs to factual inaccuracies when they encounter false information in a QnA scenario, an issue that can lead to a phenomenon we refer to as *knowledge drift*, which significantly undermines the trustworthiness of these models.
We evaluate the factuality and the uncertainty of the models' responses relying on Entropy, Perplexity, and Token Probability metrics.

Built on

Joshi, M., Choi, E., Weld, D.S., Zettlemoyer, L.: Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension. arXiv preprint arXiv:1705.03551 (2017)
Original
2017
Earlier work this paper cites.
Ott, M., Auli, M., Grangier, D., Ranzato, M.: Analyzing uncertainty in neural machine translation. In: International Conference on Machine Learning. pp. 3956–3965. PMLR (2018)
2018
Earlier work this paper cites.
Radford, A., Narasimhan, K., Salimans, T., Sutskever, I., et al.: Improving language understanding by generative pre-training (2018)
2018
Earlier work this paper cites.
Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2-7, 2019, Volume 1 (Long and Short Papers). pp. 4171–4186. Association for Computational Linguistics (2019). https://doi.org/10.18653/V1/N19-1423
2019
Earlier work this paper cites.
Petroni, F., Rocktäschel, T., Lewis, P., Bakhtin, A., Wu, Y., Miller, A.H., Riedel, S.: Language models as knowledge bases? arXiv preprint arXiv:1909.01066 (2019)
Original
2019
Earlier work this paper cites.
Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in Neural Information Processing Systems 33
2020
Earlier work this paper cites.
Desai, S., Durrett, G.: Calibration of pre-trained transformers. arXiv preprint arXiv:2003.07892 (2020)
Original
2020
Earlier work this paper cites.
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., Liu, P.J.: Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Machine Learning Research 21
2020
Earlier work this paper cites.
Roberts, A., Raffel, C., Shazeer, N.: How much knowledge can you pack into the parameters of a language model? In: Webber, B., Cohn, T., He, Y., Liu, Y. (eds.) Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, Online, November 16-20, 2020. pp. 5418–5426. Association for Computational Linguistics (2020). https://doi.org/10.18653/V1/2020.EMNLP-MAIN.437
2020
Earlier work this paper cites.

Similar

Glushkova, T., Zerva, C., Rei, R., Martins, A.F.: Uncertainty-aware machine translation evaluation. arXiv preprint arXiv:2109.06352 (2021)
Original
2021
Cited alongside, same era.
Jiang, Z., Araki, J., Ding, H., Neubig, G.: How can we know When language models know? on the calibration of language models for question answering. Trans. Assoc. Comput. Linguistics 9
2021
Cited alongside, same era.
Jiang, Z., Araki, J., Ding, H., Neubig, G.: How can we know when language models know? on the calibration of language models for question answering. Transactions of the Association for Computational Linguistics 9
2021
Cited alongside, same era.
Kadavath, S., Conerly, T., Askell, A., Henighan, T., Drain, D., Perez, E., Schiefer, N., Hatfield-Dodds, Z., DasSarma, N., Tran-Johnson, E., et al.: Language models (mostly) know what they know. arXiv preprint arXiv:2207.05221 (2022)
Original
2022
Cited alongside, same era.

Then

Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24
2023
Later among the works it cites.
Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al.: A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023)
Original
2023
Later among the works it cites.
Kuhn, L., Gal, Y., Farquhar, S.: Semantic uncertainty: Linguistic invariances for uncertainty estimation in natural language generation. In: The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenReview.net (2023)
2023
Later among the works it cites.
Ranaldi, L., Pucci, G.: When large language models contradict humans? large language models’ sycophantic behaviour. arXiv preprint arXiv:2311.09410 (2023)
Original

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

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Lin, S., Hilton, J., Evans, O.: Teaching models to express their uncertainty in words. Trans. Mach. Learn. Res. 2022

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Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F.L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al.: Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023)

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2023

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2023

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Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023)

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