ANAH-v2: Scaling Analytical Hallucination Annotation of Large Language Models

Lin, C.Y.: Rouge: A package for automatic evaluation of summaries. In: Text summarization branches out. pp. 74–81 (2004)

2004

Earlier work this paper cites.

Yang, Z., Qi, P., Zhang, S., Bengio, Y., Cohen, W.W., Salakhutdinov, R., Manning, C.D.: HotpotQA: A dataset for diverse, explainable multi-hop question answering. In: Conference on Empirical Methods in Natural Language Processing (EMNLP) (2018)

2018

Earlier work this paper cites.

Garg, S., Peitz, S., Nallasamy, U., Paulik, M.: Jointly learning to align and translate with transformer models. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). pp. 4453–4462 (2019)

2019

Earlier work this paper cites.

Durmus, E., He, H., Diab, M.: Feqa: A question answering evaluation framework for faithfulness assessment in abstractive summarization. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. pp. 5055–5070 (2020)

2020

Earlier work this paper cites.

Wang, A., Cho, K., Lewis, M.: Asking and answering questions to evaluate the factual consistency of summaries. arXiv preprint arXiv:2004.04228 (2020)

Original

2020

Earlier work this paper cites.

Weng, R., Yu, H., Wei, X., Luo, W.: Towards enhancing faithfulness for neural machine translation. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). pp. 2675–2684 (2020)

2020

Earlier work this paper cites.

Zhang*, T., Kishore*, V., Wu*, F., Weinberger, K.Q., Artzi, Y.: Bertscore: Evaluating text generation with bert. In: International Conference on Learning Representations (2020), https://openreview.net/forum?id=SkeHuCVFDr

2020

Earlier work this paper cites.

Liu, T., Zhang, Y., Brockett, C., Mao, Y., Sui, Z., Chen, W., Dolan, B.: A token-level reference-free hallucination detection benchmark for free-form text generation. arXiv preprint arXiv:2104.08704 (2021)

Original

2021

Earlier work this paper cites.

Mena, J., Pujol, O., Vitrià, J.: A survey on uncertainty estimation in deep learning classification systems from a bayesian perspective. ACM Computing Surveys (CSUR) 54

2021

Earlier work this paper cites.

Petroni, F., Piktus, A., Fan, A., Lewis, P., Yazdani, M., De Cao, N., Thorne, J., Jernite, Y., Karpukhin, V., Maillard, J., et al.: Kilt: a benchmark for knowledge intensive language tasks. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. pp. 2523–2544 (2021)

2021

Earlier work this paper cites.

Bai, Y., Kadavath, S., Kundu, S., Askell, A., Kernion, J., Jones, A., Chen, A., Goldie, A., Mirhoseini, A., McKinnon, C., et al.: Constitutional ai: Harmlessness from ai feedback. arXiv preprint arXiv:2212.08073 (2022)

Original

2022

Earlier work this paper cites.

Dziri, N., Kamalloo, E., Milton, S., Zaiane, O., Yu, M., Ponti, E.M., Reddy, S.: Faithdial: A faithful benchmark for information-seeking dialogue. Transactions of the Association for Computational Linguistics 10

2022

Earlier work this paper cites.

Gupta, P., Wu, C.S., Liu, W., Xiong, C.: Dialfact: A benchmark for fact-checking in dialogue. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). pp. 3785–3801 (2022)

2022

Earlier work this paper cites.

He, C., Li, W., Jin, Z., Wang, B., Xu, C., Lin: Opendatalab: Empowering general artificialintelligence with open datasets. https://opendatalab.com (2022)

2022

Earlier work this paper cites.

Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y., Madotto, A., Fung, P.: Survey of hallucination in natural language generation. ACM Computing Surveys (2022)

2022

Earlier work this paper cites.

Laban, P., Schnabel, T., Bennett, P.N., Hearst, M.A.: Summac: Re-visiting nli-based models for inconsistency detection in summarization. Transactions of the Association for Computational Linguistics 10

2022

Earlier work this paper cites.

Rebuffel, C., Roberti, M., Soulier, L., Scoutheeten, G., Cancelliere, R., Gallinari, P.: Controlling hallucinations at word level in data-to-text generation. Data Mining and Knowledge Discovery 36

2022

Earlier work this paper cites.

Wang, X., Wei, J., Schuurmans, D., Le, Q., Chi, E., Narang, S., Chowdhery, A., Zhou, D.: Self-consistency improves chain of thought reasoning in language models. arXiv preprint arXiv:2203.11171 (2022)

Original

2022

Earlier work this paper cites.

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)

Original

2023

Earlier work this paper cites.

Bai, J., Bai, S., Chu, Y., Cui, Z., Dang, K., Deng, X., Fan, Y., Ge, W., Han, Y., Huang, F., et al.: Qwen technical report. arXiv preprint arXiv:2309.16609 (2023)

Original

2023

Earlier work this paper cites.

Baichuan: Baichuan 2: Open large-scale language models. arXiv preprint arXiv:2309.10305 (2023), https://arxiv.org/abs/2309.10305

Original

2023

Earlier work this paper cites.

Bang, Y., Cahyawijaya, S., Lee, N., Dai, W., Su, D., Wilie, B., Lovenia, H., Ji, Z., Yu, T., Chung, W., Do, Q.V., Xu, Y., Fung, P.: A multitask, multilingual, multimodal evaluation of chatgpt on reasoning, hallucination, and interactivity. arXiv preprint arXiv:2302.04023 (2023)

Original

2023

Earlier work this paper cites.

Cao, Z., Yang, Y., Zhao, H.: Autohall: Automated hallucination dataset generation for large language models. arXiv preprint arXiv:2310.00259 (2023)

Original

2023

Earlier work this paper cites.

Chen, X., Song, D., Gui, H., Wang, C., Zhang, N., Yong, J., Huang, F., Lv, C., Zhang, D., Chen, H.: Factchd: Benchmarking fact-conflicting hallucination detection. IJCAI abs/2310.12086

Original

2023

Earlier work this paper cites.

Chen, Z., Du, W., Zhang, W., Liu, K., Liu, J., Zheng, M., Zhuo, J., Zhang, S., Lin, D., Chen, K., Zhao, F.: T-eval: Evaluating the tool utilization capability step by step. CoRR abs/2312.14033

Original

2023

Cited alongside, same era.

Chen, Z., Mao, H., Wen, H., Han, H., Jin, W., Zhang, H., Liu, H., Tang, J.: Label-free node classification on graphs with large language models (llms). arXiv preprint arXiv:2310.04668 (2023)

Original

2023

Cited alongside, same era.

Cheng, Q., Sun, T., Zhang, W., Wang, S., Liu, X., Zhang, M., He, J., Huang, M., Yin, Z., Chen, K., et al.: Evaluating hallucinations in chinese large language models. arXiv preprint arXiv:2310.03368 (2023)

Original

2023

Cited alongside, same era.

Chuang, Y.S., Xie, Y., Luo, H., Kim, Y., Glass, J., He, P.: Dola: Decoding by contrasting layers improves factuality in large language models. arXiv preprint arXiv:2309.03883 (2023)

Original

2023

Cited alongside, same era.

Semnani, S., Yao, V., Zhang, H., Lam, M.: Wikichat: Stopping the hallucination of large language model chatbots by few-shot grounding on wikipedia. In: Findings of the Association for Computational Linguistics: EMNLP 2023. pp. 2387–2413 (2023)

2023

Later among the works it cites.

Shi, W., Han, X., Lewis, M., Tsvetkov, Y., Zettlemoyer, L., Yih, S.W.t.: Trusting your evidence: Hallucinate less with context-aware decoding. arXiv preprint arXiv:2305.14739 (2023)

Original

2023

Later among the works it cites.

Singh, A., Co-Reyes, J.D., Agarwal, R., Anand, A., Patil, P., Liu, P.J., Harrison, J., Lee, J., Xu, K., Parisi, A., et al.: Beyond human data: Scaling self-training for problem-solving with language models. arXiv preprint arXiv:2312.06585 (2023)

Original

2023

Later among the works it cites.

Sun, K., Xu, Y.E., Zha, H., Liu, Y., Dong, X.L.: Head-to-tail: How knowledgeable are large language models (llm)? aka will llms replace knowledge graphs? arXiv preprint arXiv:2308.10168 (2023)

Original

2023