Hallucination Mitigation for Retrieval-Augmented Large Language Models: A Review

Survey of Hallucination in Natural Language Generation

Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., Madotto, A., & Fung, P. (2023) · ACM Computing Surveys · doi:10.1145/3571730 · Cited by 5,025 · see citing works

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. (2025) · ACM Transactions on Information Systems · doi:10.1145/3703155 · Cited by 2,142 · see citing works

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) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00276 · Cited by 2,069 · see citing works

TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension

Joshi, M., Choi, E., Weld, D., & Zettlemoyer, L. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1147 · Cited by 1,690 · see citing works

Active Retrieval Augmented Generation

Jiang, Z., Xu, F., Gao, L., Sun, Z., Liu, Q., Dwivedi-Yu, J., Yang, Y., Callan, J., & Neubig, G. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.495 · Cited by 497 · see citing works

Role play with large language models

Shanahan, M., McDonell, K., & Reynolds, L. (2023) · Nature · doi:10.1038/s41586-023-06647-8 · Cited by 491 · see citing works

In-Context Retrieval-Augmented Language Models

Ram, O., Levine, Y., Dalmedigos, I., Muhlgay, D., Shashua, A., Leyton-Brown, K., & Shoham, Y. (2023) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00605 · Cited by 449 · see citing works

Benchmarking Large Language Models for News Summarization

Zhang, T., Ladhak, F., Durmus, E., Liang, P., McKeown, K., & Hashimoto, T. B. (2024) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00632 · Cited by 415 · see citing works

Precise Zero-Shot Dense Retrieval without Relevance Labels

Gao, L., Ma, X., Lin, J., & Callan, J. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.acl-long.99 · Cited by 362 · see citing works

Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions

Trivedi, H., Balasubramanian, N., Khot, T., & Sabharwal, A. (2022) · arXiv · doi:10.48550/arxiv.2212.10509 · Cited by 349 · see citing works

Measuring and Narrowing the Compositionality Gap in Language Models

Press, O., Zhang, M., Min, S., Schmidt, L., Smith, N., & Lewis, M. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023 · doi:10.18653/v1/2023.findings-emnlp.378 · Cited by 303 · see citing works

Measuring and Narrowing the Compositionality Gap in Language Models

Press, O., Zhang, M., Min, S., Schmidt, L., Smith, N. A., & Lewis, M. (2022) · arXiv · doi:10.48550/arxiv.2210.03350 · Cited by 294 · see citing works

Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps

Ho, X., Duong Nguyen, A.-K., Sugawara, S., & Aizawa, A. (2020) · Proceedings of the 28th International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.580 · Cited by 287 · see citing works

♫ MuSiQue: Multihop Questions via Single-hop Question Composition

Trivedi, H., Balasubramanian, N., Khot, T., & Sabharwal, A. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00475 · Cited by 283 · see citing works

Jailbroken: How Does LLM Safety Training Fail?

Wei, A., Haghtalab, N., & Steinhardt, J. (2023) · arXiv · doi:10.48550/arxiv.2307.02483 · Cited by 280 · see citing works

Jailbroken: How Does LLM Safety Training Fail?

Wei, A., Haghtalab, N., & Steinhardt, J. (2023) · Advances in Neural Information Processing Systems 36 · doi:10.52202/075280-3508 · Cited by 280 · see citing works

Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Suzgun, M., Scales, N., Schärli, N., Gehrmann, S., Tay, Y., Chung, H. W., Chowdhery, A., Le, Q., Chi, E., Zhou, D., et al. (2023) · Findings of the Association for Computational Linguistics: ACL 2023 · doi:10.18653/v1/2023.findings-acl.824 · Cited by 253 · see citing works

Enabling Large Language Models to Generate Text with Citations

Gao, T., Yen, H., Yu, J., & Chen, D. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.398 · Cited by 216 · see citing works

REPLUG: Retrieval-Augmented Black-Box Language Models

Shi, W., Min, S., Yasunaga, M., Seo, M., James, R., Lewis, M., Zettlemoyer, L., & Yih, W.-T. (2024) · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) · doi:10.18653/v1/2024.naacl-long.463 · Cited by 209 · see citing works

Discovering Language Model Behaviors with Model-Written Evaluations

Perez, E., Ringer, S., Lukosiute, K., Nguyen, K., Chen, E., Heiner, S., Pettit, C., Olsson, C., Kundu, S., Kadavath, S., et al. (2023) · Findings of the Association for Computational Linguistics: ACL 2023 · doi:10.18653/v1/2023.findings-acl.847 · Cited by 194 · see citing works

LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models

Jiang, H., Wu, Q., Lin, C.-Y., Yang, Y., & Qiu, L. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.825 · Cited by 168 · see citing works

PAL: Program-aided Language Models

Gao, L., Madaan, A., Zhou, S., Alon, U., Liu, P., Yang, Y., Callan, J., & Neubig, G. (2022) · arXiv · doi:10.48550/arxiv.2211.10435 · Cited by 155 · see citing works

RARR: Researching and revising what language models say, using language models

Gao, L., Dai, Z., Pasupat, P., Chen, A., Chaganty, A. T., Fan, Y., Zhao, V., Lao, N., Lee, H., Juan, D.-C., et al. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.acl-long.910 · Cited by 141 · see citing works

Dense X Retrieval: What Retrieval Granularity Should We Use?

Chen, T., Wang, H., Chen, S., Yu, W., Ma, K., Zhao, X., Zhang, H., & Yu, D. (2024) · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2024.emnlp-main.845 · Cited by 74 · see citing works

FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation

Vu, T., Iyyer, M., Wang, X., Constant, N., Wei, J., Wei, J., Tar, C., Sung, Y.-H., Zhou, D., Le, Q., et al. (2024) · Findings of the Association for Computational Linguistics ACL 2024 · doi:10.18653/v1/2024.findings-acl.813 · Cited by 73 · see citing works

On the Risk of Misinformation Pollution with Large Language Models

Pan, Y., Pan, L., Chen, W., Nakov, P., Kan, M.-Y., & Wang, W. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023 · doi:10.18653/v1/2023.findings-emnlp.97 · Cited by 70 · see citing works

RealTime QA: What's the Answer Right Now?

Kasai, J., Sakaguchi, K., Takahashi, Y., Bras, R. L., Asai, A., Yu, X., Radev, D., Smith, N. A., Choi, Y., & Inui, K. (2022) · arXiv · doi:10.48550/arxiv.2207.13332 · Cited by 52 · see citing works

Assisting in Writing Wikipedia-like Articles From Scratch with Large Language Models

Shao, Y., Jiang, Y., Kanell, T., Xu, P., Khattab, O., & Lam, M. (2024) · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) · doi:10.18653/v1/2024.naacl-long.347 · Cited by 41 · see citing works

Rich Knowledge Sources Bring Complex Knowledge Conflicts: Recalibrating Models to Reflect Conflicting Evidence

Chen, H.-T., Zhang, M. J. Q., & Choi, E. (2022) · arXiv · doi:10.48550/arxiv.2210.13701 · Cited by 31 · see citing works

OctoPack: Instruction Tuning Code Large Language Models

Muennighoff, N., Liu, Q., Zebaze, A., Zheng, Q., Hui, B., Zhuo, T. Y., Singh, S., Tang, X., von Werra, L., & Longpre, S. (2023) · arXiv · doi:10.48550/arxiv.2308.07124 · Cited by 17 · see citing works

Probabilistic Tree-of-thought Reasoning for Answering Knowledge-intensive Complex Questions

Cao, S., Zhang, J., Shi, J., Lv, X., Yao, Z., Tian, Q., Hou, L., & Li, J. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023 · doi:10.18653/v1/2023.findings-emnlp.835 · Cited by 17 · see citing works

The Earth is Flat because...: Investigating LLMs’ Belief towards Misinformation via Persuasive Conversation

Xu, R., Lin, B., Yang, S., Zhang, T., Shi, W., Zhang, T., Fang, Z., Xu, W., & Qiu, H. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2024.acl-long.858 · Cited by 17 · see citing works

Beamaggr: Beam aggregation reasoning over multi-source knowledge for multi-hop question answering

Chu, Z., Chen, J., Chen, Q., Wang, H., Zhu, K., Du, X., Yu, W., Liu, M., & Qin, B. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2024.acl-long.67 · Cited by 11 · see citing works

From Illusion to Insight: A Taxonomic Survey of Hallucination Mitigation Techniques in LLMs

Kazlaris, I., Antoniou, E., Diamantaras, K., & Bratsas, C. (2025) · doi:10.20944/preprints202508.1942.v1 · Cited by 13 · see citing works

Mitigating Hallucination in Large Language Models: A Capability-Oriented Survey on RAG, Reasoning, and Agentic Systems

Li, Y., Fu, X., Verma, G., Buitelaar, P., & Liu, M. (2025) · arXiv · doi:10.48550/arxiv.2510.24476 · Cited by 4 · see citing works

Bi-Mem: Bidirectional Construction of Hierarchical Memory for Personalized LLMs via Inductive-Reflective Agents

Mao, W., Tan, H., Liu, S., Liu, H., Xu, Y., Ji, H., & Wang, X. (2026) · arXiv · doi:10.48550/arxiv.2601.06490

Citation-Enforced RAG for Fiscal Document Intelligence: Cited, Explainable Knowledge Retrieval in Tax Compliance

Shanivendra, A. C. (2026) · arXiv · doi:10.48550/arxiv.2603.14170