Эта статья является препринтом и не была отрецензирована.
О результатах, изложенных в препринтах, не следует сообщать в СМИ как о проверенной информации.
Genomic Convergence Verification: A Multi-Agent Framework for Hallucination Detection in Retrieval-Augmented Generation. Overcoming Gödel's Limitation Through Multi-System Convergence.
1. K. Gödel. Über formal unentscheidbare Sätze der Principia Mathematica und verwandter Systeme I. Monatshefte für Mathematik und Physik, 38:173-198, 1931.
2. P. Lewis, E. Perez, A. Piktus, F. Petroni, V. Karpukhin, N. Goyal, H. Küttler, M. Lewis, W. Yih, T. Rocktäschel, S. Riedel, and D. Kiela. Retrieval-augmented generation for knowledge-intensive NLP tasks. In Advances in Neural Information Processing Systems (NeurIPS), volume 33, pages 9459-9474, 2020.
3. J. Maynez, S. Narayan, B. Bohnet, and R. McDonald. On faithfulness and factuality in abstractive summarization. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL), pages 1906-1919, 2020.
4. K. Shuster, S. Poff, M. Chen, D. Kiela, and J. Weston. Retrieval augmentation reduces hallucination in conversation. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 3784-3803, 2021.
5. W. Kryściński, B. McCann, C. Xiong, and R. Socher. Evaluating the factual consistency of abstractive text summarization. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 9332-9346, 2020.
6. P. Laban, T. Schnabel, P. N. Bennett, and M. A. Hearst. SummaC: Re-visiting NLI-based models for inconsistency detection in summarization. Transactions of the Association for Computational Linguistics, 10:163-177, 2022.
7. Y. Zha, Y. Yang, R. Li, and Z. Hu. AlignScore: Evaluating factual consistency with a unified alignment function. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL), 2023.
8. P. Manakul, A. Liusie, and M. J. F. Gales. SelfCheckGPT: Zero-resource black-box hallucination detection for generative large language models. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2023.
9. R. Cohen, M. Hamri, M. Geva, and A. Globerson. LM vs LM: Detecting factual errors via cross examination. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2023.
10. Y. Du, S. Li, A. Torralba, J. Tenenbaum, and I. Mordatch. Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325, 2023.
11. J. Thorne, A. Vlachos, C. Christodoulopoulos, and A. Mittal. FEVER: a large-scale dataset for fact extraction and verification. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), pages 809-819, 2018.
12. Z. Guo, M. Schlichtkrull, and A. Vlachos. A survey on automated fact-checking. Transactions of the Association for Computational Linguistics, 10:178-206, 2022.
13. N. Reimers and I. Gurevych. Sentence-BERT: Sentence embeddings using Siamese BERT-networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP-IJCNLP), pages 3982-3992, 2019.
14. J. Li, X. Cheng, W. X. Zhao, J. Y. Nie, and J. R. Wen. HaluEval: A large-scale hallucination evaluation benchmark for large language models. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2023.
15. Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer, and V. Stoyanov. RoBERTa: A robustly optimized BERT pretraining approach. arXiv preprint arXiv:1907.11692, 2019.