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

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

Zhang, W., & Zhang, J. (2025) · Mathematics · doi:10.3390/math13050856 · Cited by 145 · see citing works

Knowledge Conflicts for LLMs: A Survey

Xu, R., Qi, Z., Guo, Z., Wang, C., Wang, H., Zhang, Y., & Xu, W. (2024) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2024.emnlp-main.486 · Cited by 139 · see citing works

When helpfulness backfires: LLMs and the risk of false medical information due to sycophantic behavior

Chen, S., Gao, M., Sasse, K., Hartvigsen, T., Anthony, B., Fan, L., Aerts, H., Gallifant, J., & Bitterman, D. S. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02008-z · Cited by 112 · see citing works

How Johnny Can Persuade LLMs to Jailbreak Them: Rethinking Persuasion to Challenge AI Safety by Humanizing LLMs

Zeng, Y., Lin, H., Zhang, J., Yang, D., Jia, R., & Shi, W. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2024.acl-long.773 · Cited by 107 · see citing works

DELL: Generating Reactions and Explanations for LLM-Based Misinformation Detection

Wan, H., Feng, S., Tan, Z., Wang, H., Tsvetkov, Y., & Luo, M. (2024) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2024.findings-acl.155 · Cited by 81 · see citing works

Let Silence Speak: Enhancing Fake News Detection with Generated Comments from Large Language Models

Nan, Q., Sheng, Q., Cao, J., Hu, B., Wang, D., & Li, J. (2024) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3627673.3679519 · Cited by 72 · see citing works

Know Your Limits: A Survey of Abstention in Large Language Models

Wen, B., Yao, J., Feng, S., Xu, C., Tsvetkov, Y., Howe, B., & Wang, L. L. (2025) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00754 · Cited by 70 · see citing works

What Does the Bot Say? Opportunities and Risks of Large Language Models in Social Media Bot Detection

Feng, S., Wan, H., Wang, N., Tan, Z., Luo, M., & Tsvetkov, Y. (2024) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2024.acl-long.196 · Cited by 38 · see citing works

Trust Me, I’m Wrong: LLMs Hallucinate with Certainty Despite Knowing the Answer

Simhi, A., Itzhak, I., Barez, F., Stanovsky, G., & Belinkov, Y. (2025) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2025.findings-emnlp.792 · Cited by 30 · see citing works

Language Models in Dialogue: Conversational Maxims for Human-AI Interactions

Miehling, E., Nagireddy, M., Sattigeri, P., Daly, E., Piorkowski, D., & Richards, J. T. (2024) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2024.findings-emnlp.843 · Cited by 26 · see citing works

From Predictions to Analyses: Rationale-Augmented Fake News Detection with Large Vision-Language Models

Zheng, X., Zeng, Z., Wang, H., Bai, Y., Liu, Y., & Luo, M. (2025) · International World Wide Web Conference (WWW) · doi:10.1145/3696410.3714532 · Cited by 19 · see citing works

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

Kazlaris, I., Antoniou, E. N., Diamantaras, K., & Bratsas, C. (2025) · AI · doi:10.3390/ai6100260 · Cited by 19 · see citing works

Kernels of selfhood: GPT-4o shows humanlike patterns of cognitive dissonance moderated by free choice

Lehr, S. A., Saichandran, K. S., Harmon‐Jones, E., Vitali, N., & Banaji, M. R. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2501823122 · Cited by 16 · see citing works

Must Read: A Comprehensive Survey of Computational Persuasion

Bozdag, N. B., Mehri, S., Yang, X., Ha, H., Cheng, Z., Durmus, E., You, J., Ji, H., Tür, G., & Hakkani-Tur, D. Z. (2026) · ACM Computing Surveys · doi:10.1145/3800687 · Cited by 14 · see citing works

“They are uncultured”: Unveiling Covert Harms and Social Threats in LLM Generated Conversations

Dammu, P. P. S., Jung, H., Singh, A. A. K., Choudhury, M., & Mitra, T. (2024) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2024.emnlp-main.1134 · Cited by 12 · see citing works

A Survey on Automatic Credibility Assessment Using Textual Credibility Signals in the Era of Large Language Models

Srba, I., Razuvayevskaya, O., Leite, J. A., Moro, R., Schlicht, I. B., Tonelli, S., García, F. M., Lottmann, S. B., Teyssou, D., Porcellini, V., et al. (2026) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3770077 · Cited by 19 · see citing works

Persuade Me if You Can: A Framework for Evaluating Persuasion Effectiveness and Susceptibility Among Large Language Models

Bozdag, N. B., Mehri, S., Tür, G., & Hakkani-Tur, D. Z. (2026) · ACM Conference on AI and Agentic Systems (CAIS) · doi:10.1145/3786335.3813181 · Cited by 9 · see citing works

Catching Chameleons: Detecting Evolving Disinformation Generated using Large Language Models

Jiang, B., Zhao, C., Tan, Z., & Liu, H. (2024) · IEEE International Conference on Cognitive Machine Intelligence (CogMI) · doi:10.1109/cogmi62246.2024.00034 · Cited by 6 · see citing works

Misinformation and Disinformation in Generative AI—A Survey

Srinivasan, R. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-8183-9_23 · Cited by 5 · see citing works

Firm or Fickle? Evaluating Large Language Models Consistency in Sequential Interactions

Li, Y., Miao, Y., Ding, X., Krishnan, R., & Padman, R. (2025) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2025.findings-acl.347 · Cited by 5 · see citing works

Detecting Misinformation by Uncovering Commonsense Conflicts With LLM Workflows

Wang, B., Li, X., Chang-chun, L., Zhao, B., Guan, R., Wu, L. Y., & Han, J. (2026) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2025.3650588 · Cited by 4 · see citing works

When AI reviews science: Can we trust the referee?

Wang, J., Liu, Y., Xu, H., Hu, K., Di, S., Ni, W., Yue, L., Zhang, M.-L., Ren, K., & Chen, L. (2026) · The Innovation Informatics · doi:10.59717/j.xinn-inform.2026.100030 · Cited by 4 · see citing works

Cutting through the noise to motivate people: A comprehensive analysis of COVID-19 social media posts de/motivating vaccination

Rahman, A., Mohammadi, E., & Alhoori, H. (2024) · Natural Language Processing Journal · doi:10.1016/j.nlp.2024.100085 · Cited by 3 · see citing works

Aligning Large Language Models for Faithful Integrity Against Opposing Argument

Zhao, Y., Deng, Y., Ng, S.-K., & Chua, T. (2025) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v39i26.34990 · Cited by 2 · see citing works

Enhancing belief consistency of large language model agents in decision-making process based on attribution theory

He, G., Zhang, M., Su, T., Ma, L., & Zhu, X. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.129273 · Cited by 2 · see citing works

AI-Facilitated Coercive Control: An Experimental Study

Kim, H., Ristenpart, T., & Dell, N. L. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790859 · Cited by 2 · see citing works

Beware of the Woozle Effect: Exploring and Mitigating Hallucination Propagation in Multi-Agent Debate

Zhang, Z., Zhang, Y., Zeng, D., Liu, K., & Zhao, J. (2026) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2026.3675803 · Cited by 1 · see citing works

Beyond Accuracy: Experts See AI Fact-Checks as Accurate but Less Useful

Jia, C., Gondimalla, A., Zhang, A., Mullings, D. J., Boltz, A., & Lee, M. K. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791625 · Cited by 1 · see citing works

When RAG and LLM Conflict: Who Will AI Listen to?

Fang, Q. (2024) · doi:10.59350/7gfzv-88m36

A Method for Detecting Knowledge Conflicts in Chinese Intelligent Agent Interactions

Cheng, H., Gu, Y., Xi, M., Zhong, Q., & Liu, W. (2025) · Interdisciplinary Journal of Information Knowledge and Management · doi:10.28945/5497

GOODLIAR: A Reinforcement Learning-Based Deceptive Agent for Disrupting LLM Beliefs on Foundational Principles

Kim, S. K., & Cho, H. (2025) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2025.findings-acl.160

Phi: Preference Hijacking in Multi-modal Large Language Models at Inference Time

Lan, Y., Cao, Y., Zhang, W., Lü, L., & Chen, J. (2025) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2025.emnlp-main.901

TurnBack: A Geospatial Route Cognition Benchmark for Large Language Models through Reverse Route

Luo, H., Cheng, Q., Matos, D., Gadi, H. K., Zhang, Y., Liu, L., Wang, Y., Zeller, N., Cremers, D., & Meng, L. (2025) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2025.emnlp-main.440

Temporal Conflicts in LLMs: Reproducibility Insights from Unifying DYNAMICQA and MULAN

Dey, R., Ounis, I., McDonald, G., & Moshfeghi, Y. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-21324-2_8

Fallibility, persuadability, and correctability of large language models under sustained conversational misinformation pressure

Rodriguez, J., Hansen, Z., Anda, L. D., Rohrer, K., Grubb, C., Noriega-Atala, E., Surdeanu, M., & Slepian, M. J. (2026) · Scientific Reports · doi:10.1038/s41598-026-68231-0

When machines misread science: creating guardrails for human and AI interpretation of biomedical research

Peoples, N., Milne, W. K., Luo, B., & Yan, L. L. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-03160-w

Friendlier LLMs tell users what they want to hear — even when it is wrong

Ong, D. C. (2026) · Nature · doi:10.1038/d41586-026-01153-z

To Lie or Not to Lie? Investigating The Biased Spread of Global Lies by LLMs

Khan, Z., Dogan, M., Okoh, I., Sadeghi, P., Shrestha, S., Nyah, S. J., Mokhiamar, M. O., Ryan, M. J., & Naous, T. (2026) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2026.acl-long.695

Whose Line is it Anyway? Linguistic Attribution of Misinformation from Mainstream and Fringe LLMs

Kim, H. K., Garg, A., Kim, H., Joo, E., & Skumanich, A. (2025) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata66926.2025.11402634

Fallibility, persuadability, and correctability of large language models under sustained conversational misinformation pressure

Rodriguez, J., Hansen, Z., Anda, L. D., Rohrer, K., Grubb, C., Noriega-Atala, E., Surdeanu, M., & Slepian, M. J. (2026) · Scientific Reports · doi:10.1038/s41598-026-68231-0

Deceptive Explanations by Large Language Models Lead People to Change their Beliefs About Misinformation More Often than Honest Explanations

Danry, V., Pataranutaporn, P., Groh, M., & Epstein, Z. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713408 · Cited by 35 · see citing works