Built on

H. Zhang, “mixup: Beyond empirical risk minimization,” arXiv preprint arXiv:1710.09412 , 2017
Original
2017
Earlier work this paper cites.
M. Sensoy, L. Kaplan, and M. Kandemir, “Evidential deep learning to quantify classification uncertainty,” Advances in neural information processing systems , vol. 31, 2018
2018
Earlier work this paper cites.
S. Mohammad, F. Bravo-Marquez, M. Salameh, and S. Kiritchenko, “SemEval-2018 task 1: Affect in tweets,” in Proceedings of the 12th International Workshop on Semantic Evaluation , M. Apidianaki, S. M. Mohammad, J. May, E. Shutova, S. Bethard, and M. Carpuat, Eds. New Orleans, Louisiana: Association for Computational Linguistics, Jun. 2018, pp. 1–17. [Online]. Available: https://aclanthology.org/S18-1001/
2018
Earlier work this paper cites.
Y. Xiao and W. Y. Wang, “On hallucination and predictive uncertainty in conditional language generation,” 2021. [Online]. Available: https://arxiv.org/abs/2103.15025
Original
2021
Earlier work this paper cites.
U. Arora, W. Huang, and H. He, “Types of out-of-distribution texts and how to detect them,” in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing , 2021, pp. 10 687–10 701
2021
Earlier work this paper cites.
A. Malinin and M. Gales, “Uncertainty estimation in autoregressive structured prediction,” in International Conference on Learning Representations , 2021. [Online]. Available: https://openreview.net/forum?id=jN5y-zb5Q7m
2021
Earlier work this paper cites.
M. Abdar, F. Pourpanah, S. Hussain, D. Rezazadegan, L. Liu, M. Ghavamzadeh, P. Fieguth, X. Cao, A. Khosravi, U. R. Acharya et al. , “A review of uncertainty quantification in deep learning: Techniques, applications and challenges,” Information fusion , vol. 76, pp. 243–297, 2021
2021
Earlier work this paper cites.
S. Lin, J. Hilton, and O. Evans, “Truthfulqa: Measuring how models mimic human falsehoods,” arXiv preprint arXiv:2109.07958 , 2021
Original
2021
Earlier work this paper cites.
S. Kadavath, T. Conerly, A. Askell, T. Henighan, D. Drain, E. Perez, N. Schiefer, Z. Hatfield-Dodds, N. DasSarma, E. Tran-Johnson et al. , “Language models (mostly) know what they know,” arXiv preprint arXiv:2207.05221 , 2022
Original
2022
Earlier work this paper cites.
H. Wei, R. Xie, H. Cheng, L. Feng, B. An, and Y. Li, “Mitigating neural network overconfidence with logit normalization,” in International conference on machine learning . PMLR, 2022, pp. 23 631–23 644
2022
Earlier work this paper cites.
L. Huang, W. Yu, W. Ma, W. Zhong, Z. Feng, H. Wang, Q. Chen, W. Peng, X. Feng, B. Qin et al. , “A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions,” arXiv preprint arXiv:2311.05232 , 2023
Original
2023
Earlier work this paper cites.
T. Zhang, L. Qiu, Q. Guo, C. Deng, Y. Zhang, Z. Zhang, C. Zhou, X. Wang, and L. Fu, “Enhancing uncertainty-based hallucination detection with stronger focus,” arXiv preprint arXiv:2311.13230 , 2023
Original
2023
Earlier work this paper cites.
Z. Ji, T. Yu, Y. Xu, N. Lee, E. Ishii, and P. Fung, “Towards mitigating llm hallucination via self reflection,” in Findings of the Association for Computational Linguistics: EMNLP 2023 , 2023, pp. 1827–1843
2023
Earlier work this paper cites.

Similar

J. R. Cole, M. J. Q. Zhang, D. Gillick, J. M. Eisenschlos, B. Dhingra, and J. Eisenstein, “Selectively answering ambiguous questions,” 2023. [Online]. Available: https://arxiv.org/abs/2305.14613
Original
2023
Cited alongside, same era.
D. Ulmer, C. Hardmeier, and J. Frellsen, “Prior and posterior networks: A survey on evidential deep learning methods for uncertainty estimation,” 2023. [Online]. Available: https://arxiv.org/abs/2110.03051
Original
2023
Cited alongside, same era.
S. Banerjee, A. Agarwal, and S. Singla, “Llms will always hallucinate, and we need to live with this,” arXiv preprint arXiv:2409.05746 , 2024
Original
2024
Cited alongside, same era.
S. V. Shah, “Accuracy, consistency, and hallucination of large language models when analyzing unstructured clinical notes in electronic medical records,” JAMA Network Open , vol. 7, no. 8, pp. e2 425 953–e2 425 953, 2024
2024
Cited alongside, same era.

Then

N. Gupta, H. Narasimhan, W. Jitkrittum, A. S. Rawat, A. K. Menon, and S. Kumar, “Language model cascades: Token-level uncertainty and beyond,” arXiv preprint arXiv:2404.10136 , 2024
Original
2024
Later among the works it cites.
E. Fadeeva, A. Rubashevskii, A. Shelmanov, S. Petrakov, H. Li, H. Mubarak, E. Tsymbalov, G. Kuzmin, A. Panchenko, T. Baldwin et al. , “Fact-checking the output of large language models via token-level uncertainty quantification,” arXiv preprint arXiv:2403.04696 , 2024
Original
2024
Later among the works it cites.
Z. Lin, T. Liang, J. Xu, X. Wang, R. Luo, C. Shi, S. Li, Y. Yang, and Z. Tu, “Critical tokens matter: Token-level contrastive estimation enhence llm’s reasoning capability,” arXiv preprint arXiv:2411.19943 , 2024
Original
2024
Later among the works it cites.
J. Duan, H. Cheng, S. Wang, A. Zavalny, C. Wang, R. Xu, B. Kailkhura, and K. Xu, “Shifting attention to relevance: Towards the predictive uncertainty quantification of free-form large language models,” in Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , 2024, pp. 5050–5063

Beyond the bibliography

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

Open on alphaXiv

alphaXiv is searching for related work…

Reading the bibliography…

2025

Estimating LLM Uncertainty with Evidence

Ma, Huan, Chen, Jingdong, Zhou, Joey Tianyi et al.

Understand

Over the past few years, Large Language Models (LLMs) have developed rapidly and are widely applied in various domains.

However, LLMs face the issue of hallucinations, generating responses that may be unreliable when the models lack relevant knowledge.
To be aware of potential hallucinations, uncertainty estimation methods have been introduced, and most of them have confirmed that reliability lies in critical tokens.
However, probability-based methods perform poorly in identifying token reliability, limiting their practical utility.

Built on

H. Zhang, “mixup: Beyond empirical risk minimization,” arXiv preprint arXiv:1710.09412 , 2017
Original
2017
Earlier work this paper cites.
M. Sensoy, L. Kaplan, and M. Kandemir, “Evidential deep learning to quantify classification uncertainty,” Advances in neural information processing systems , vol. 31, 2018
2018
Earlier work this paper cites.
S. Mohammad, F. Bravo-Marquez, M. Salameh, and S. Kiritchenko, “SemEval-2018 task 1: Affect in tweets,” in Proceedings of the 12th International Workshop on Semantic Evaluation , M. Apidianaki, S. M. Mohammad, J. May, E. Shutova, S. Bethard, and M. Carpuat, Eds. New Orleans, Louisiana: Association for Computational Linguistics, Jun. 2018, pp. 1–17. [Online]. Available: https://aclanthology.org/S18-1001/
2018
Earlier work this paper cites.
Y. Xiao and W. Y. Wang, “On hallucination and predictive uncertainty in conditional language generation,” 2021. [Online]. Available: https://arxiv.org/abs/2103.15025
Original
2021
Earlier work this paper cites.
U. Arora, W. Huang, and H. He, “Types of out-of-distribution texts and how to detect them,” in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing , 2021, pp. 10 687–10 701
2021
Earlier work this paper cites.
A. Malinin and M. Gales, “Uncertainty estimation in autoregressive structured prediction,” in International Conference on Learning Representations , 2021. [Online]. Available: https://openreview.net/forum?id=jN5y-zb5Q7m
2021
Earlier work this paper cites.
M. Abdar, F. Pourpanah, S. Hussain, D. Rezazadegan, L. Liu, M. Ghavamzadeh, P. Fieguth, X. Cao, A. Khosravi, U. R. Acharya et al. , “A review of uncertainty quantification in deep learning: Techniques, applications and challenges,” Information fusion , vol. 76, pp. 243–297, 2021
2021
Earlier work this paper cites.
S. Lin, J. Hilton, and O. Evans, “Truthfulqa: Measuring how models mimic human falsehoods,” arXiv preprint arXiv:2109.07958 , 2021
Original
2021
Earlier work this paper cites.
S. Kadavath, T. Conerly, A. Askell, T. Henighan, D. Drain, E. Perez, N. Schiefer, Z. Hatfield-Dodds, N. DasSarma, E. Tran-Johnson et al. , “Language models (mostly) know what they know,” arXiv preprint arXiv:2207.05221 , 2022
Original
2022
Earlier work this paper cites.
H. Wei, R. Xie, H. Cheng, L. Feng, B. An, and Y. Li, “Mitigating neural network overconfidence with logit normalization,” in International conference on machine learning . PMLR, 2022, pp. 23 631–23 644
2022
Earlier work this paper cites.
L. Huang, W. Yu, W. Ma, W. Zhong, Z. Feng, H. Wang, Q. Chen, W. Peng, X. Feng, B. Qin et al. , “A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions,” arXiv preprint arXiv:2311.05232 , 2023
Original
2023
Earlier work this paper cites.
T. Zhang, L. Qiu, Q. Guo, C. Deng, Y. Zhang, Z. Zhang, C. Zhou, X. Wang, and L. Fu, “Enhancing uncertainty-based hallucination detection with stronger focus,” arXiv preprint arXiv:2311.13230 , 2023
Original
2023
Earlier work this paper cites.
Z. Ji, T. Yu, Y. Xu, N. Lee, E. Ishii, and P. Fung, “Towards mitigating llm hallucination via self reflection,” in Findings of the Association for Computational Linguistics: EMNLP 2023 , 2023, pp. 1827–1843
2023
Earlier work this paper cites.

Similar

J. R. Cole, M. J. Q. Zhang, D. Gillick, J. M. Eisenschlos, B. Dhingra, and J. Eisenstein, “Selectively answering ambiguous questions,” 2023. [Online]. Available: https://arxiv.org/abs/2305.14613
Original
2023
Cited alongside, same era.
D. Ulmer, C. Hardmeier, and J. Frellsen, “Prior and posterior networks: A survey on evidential deep learning methods for uncertainty estimation,” 2023. [Online]. Available: https://arxiv.org/abs/2110.03051
Original
2023
Cited alongside, same era.
S. Banerjee, A. Agarwal, and S. Singla, “Llms will always hallucinate, and we need to live with this,” arXiv preprint arXiv:2409.05746 , 2024
Original
2024
Cited alongside, same era.
S. V. Shah, “Accuracy, consistency, and hallucination of large language models when analyzing unstructured clinical notes in electronic medical records,” JAMA Network Open , vol. 7, no. 8, pp. e2 425 953–e2 425 953, 2024
2024
Cited alongside, same era.

Then

N. Gupta, H. Narasimhan, W. Jitkrittum, A. S. Rawat, A. K. Menon, and S. Kumar, “Language model cascades: Token-level uncertainty and beyond,” arXiv preprint arXiv:2404.10136 , 2024
Original
2024
Later among the works it cites.
E. Fadeeva, A. Rubashevskii, A. Shelmanov, S. Petrakov, H. Li, H. Mubarak, E. Tsymbalov, G. Kuzmin, A. Panchenko, T. Baldwin et al. , “Fact-checking the output of large language models via token-level uncertainty quantification,” arXiv preprint arXiv:2403.04696 , 2024
Original
2024
Later among the works it cites.
Z. Lin, T. Liang, J. Xu, X. Wang, R. Luo, C. Shi, S. Li, Y. Yang, and Z. Tu, “Critical tokens matter: Token-level contrastive estimation enhence llm’s reasoning capability,” arXiv preprint arXiv:2411.19943 , 2024
Original
2024
Later among the works it cites.
J. Duan, H. Cheng, S. Wang, A. Zavalny, C. Wang, R. Xu, B. Kailkhura, and K. Xu, “Shifting attention to relevance: Towards the predictive uncertainty quantification of free-form large language models,” in Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , 2024, pp. 5050–5063

Beyond the bibliography

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

Open on alphaXiv

alphaXiv is searching for related work…

M. Dahl, V. Magesh, M. Suzgun, and D. E. Ho, “Large legal fictions: Profiling legal hallucinations in large language models,” Journal of Legal Analysis , vol. 16, no. 1, pp. 64–93, 2024

2024

Cited alongside, same era.

F. Liu, Y. Liu, L. Shi, H. Huang, R. Wang, Z. Yang, L. Zhang, Z. Li, and Y. Ma, “Exploring and evaluating hallucinations in llm-powered code generation,” arXiv preprint arXiv:2404.00971 , 2024

Original

2024

Cited alongside, same era.

L. Zhou, W. Schellaert, F. Martínez-Plumed, Y. Moros-Daval, C. Ferri, and J. Hernández-Orallo, “Larger and more instructable language models become less reliable,” Nature , pp. 1–8, 2024

2024

Cited alongside, same era.

H.-Y. Huang, Y. Yang, Z. Zhang, S. Lee, and Y. Wu, “A survey of uncertainty estimation in llms: Theory meets practice,” arXiv preprint arXiv:2410.15326 , 2024

Original

2024

Cited alongside, same era.

L. Yoffe, A. Amayuelas, and W. Y. Wang, “Debunc: mitigating hallucinations in large language model agent communication with uncertainty estimations,” arXiv preprint arXiv:2407.06426 , 2024

Original

2024

Cited alongside, same era.

J. Zhang, Z. Li, K. Das, B. A. Malin, and S. Kumar, “Sac3: Reliable hallucination detection in black-box language models via semantic-aware cross-check consistency,” 2024. [Online]. Available: https://arxiv.org/abs/2311.01740

Original

2024

Cited alongside, same era.

H.-Y. Huang, Z. Wu, Y. Yang, J. Zhang, and Y. Wu, “Unc-ttp: A method for classifying llm uncertainty to improve in-context example selection,” 2024. [Online]. Available: https://arxiv.org/abs/2408.09172

Original

2024

Cited alongside, same era.

L. Kuhn, Y. Gal, and S. Farquhar, “Semantic uncertainty: Linguistic invariances for uncertainty estimation in natural language generation,” Nature , 2024

2024

Cited alongside, same era.

2024