---
title: "Deconstruct, Diagnose, and Deliberate: A Protocol-Adaptive"
description: "The authors propose PARD, a multi-agent framework that splits verification into factual, logical and contextual analysis. 46 references."
authors: ["Zhigan Zhang", "Jie Sui"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: ACL 2026"
source: "https://aclanthology.org/2026.findings-acl.1227/"
doi: "10.18653/v1/2026.findings-acl.1227"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/zhang-2026-deconstruct-diagnose-deliberate-protocol-adaptive/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/zhang-2026-deconstruct-diagnose-deliberate-protocol-adaptive/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2026](https://md.research.elamur.ai/free/en/papers/2026/page/29/) / Deconstruct, Diagnose, and Deliberate: A Protocol-Adaptive Role-Specific Multi-Agent Framework for Fake News Detection

# Deconstruct, Diagnose, and Deliberate: A Protocol-Adaptive Role-Specific Multi-Agent Framework for Fake News Detection

**Authors:** Zhigan Zhang and Jie Sui · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: ACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.findings-acl.1227/) · [PDF](https://aclanthology.org/2026.findings-acl.1227.pdf) · [DOI 10.18653/v1/2026.findings-acl.1227](https://doi.org/10.18653/v1/2026.findings-acl.1227) · [OpenAlex](https://openalex.org/W7166858739)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Modern misinformation rarely relies on one false fact. It tends to combine distorted evidence, faulty reasoning and manipulative framing, which single-model detectors and stance-only debate systems handle poorly. The authors propose PARD, a multi-agent framework that splits verification into factual, logical and contextual analysis.

In the Diagnose phase, heterogeneous candidate LLMs are scored in two ways: an in-domain "talent" test graded by a critic LLM, and role-specific benchmark tests. A team is then chosen that balances role fit with diversity. In the Deliberate phase, the three analyzers first report independently. A Strategy Planner then chooses an interaction protocol, drawing on a reinforcement-learning policy and a Strategic Experience Memory. An Arbiter and a Summarizer produce the verdict, and discussion stops early once agents converge.

For evaluation, the authors release LIAR-RAW+, which adds fine-grained factual, logical and contextual annotations to LIAR-RAW. PARD outperforms baselines in both prediction accuracy and explanation quality while needing fewer training instances than fully supervised models. This makes it relevant for interpretable, auditable fact-checking.

## Key points
- The protocol library has three options: Round-Robin Discussion, Point-Counterpoint Dialogue and Cross-Examination.
- The talent test uses LIAR-RAW and RAWFC claims scored on a 5-point Likert scale. The factual performance test uses FEVER with Wikipedia retrieval and is measured as an evidence-coverage F1.
- Early stopping fired for 47.38% of claims and cut average rounds from 3.26 to 2.14. It also improved the correction score compared with forcing all rounds (0.6329 vs. 0.5763).
- The new PolitiFact 2025 benchmark contains 450 claims published after the models' knowledge cutoff. On it, PARD reaches 32.82 macro-F1, compared with 29.38 for a single agent and 31.70 for a homogeneous team.
- Stated limitations: PARD handles text only, lacks real-time knowledge retrieval, and remains exposed to adversarial manipulation. Code is available at github.com/zigzag2025/PARD.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [University of Chinese Academy of Sciences](https://md.research.elamur.ai/free/en/universities/university-of-chinese-academy-of-sciences/) — University, Beijing, China · 188 works · 8,743 citations

📚 Shelves (5): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 18,614 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 17,965 works · [🤝 Social Sciences](https://md.research.elamur.ai/free/en/shelves/social-sciences/) · 1,954 works · [🗄️ Information Systems](https://md.research.elamur.ai/free/en/shelves/computer-science/information-systems/) · 1,730 works · [🤝 Sociology and Political Science](https://md.research.elamur.ai/free/en/shelves/social-sciences/sociology-and-political-science/) · 626 works

## Cite

Cite as: Zhigan Zhang and Jie Sui. 2026. Deconstruct, Diagnose, and Deliberate: A Protocol-Adaptive Role-Specific Multi-Agent Framework for Fake News Detection. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{zhang2026deconstruct,
  title = {{Deconstruct, Diagnose, and Deliberate: A Protocol-Adaptive Role-Specific Multi-Agent Framework for Fake News Detection}},
  author = {Zhigan Zhang and Jie Sui},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.1227},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.1227}
}
```

- [← previous Decomposing Sign Language Movements: A Multi-Band Visualization Method for Articulatory Analysis](https://md.research.elamur.ai/free/en/papers/sevilla-2026-decomposing-sign-language-movements-multi/)
- [next → Deconstructing Instruction-Following: A New Benchmark for Granular Evaluation of Large Language Model Instruction Compliance Abilities](https://md.research.elamur.ai/free/en/papers/purpura-2026-deconstructing-instruction-following-new-benchmark/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/29/)

## References

46 references, 20 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Pepa Atanasova, Jakob Grue Simonsen, Christina Lioma, and Isabelle Augenstein. 2020. [Generating fact checking explanations](https://md.research.elamur.ai/free/en/papers/atanasova-2020-generating-fact-checking-explanations/). In Proceedings of the 58th annual meeting of the association for computational linguistics, pages 7352–7364.
2. Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter,… [find](https://scholar.google.com/scholar?q=Tom+B%2E+Brown%2C+Benjamin+Mann%2C+Nick+Ryder%2C+Melanie+Subbiah%2C+Jared+Kaplan%2C+Prafulla+Dhariwal%2C+Arvind+Neelakantan%2C+Pranav+Shyam%2C+Girish+Sastry%2C+Amanda+Askell%2C+Sandhini+Agarwal%2C+Ariel+Herbert%2DVoss%2C+Gretche)
3. Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, Harsha Nori, Hamid Palangi, Marco Tulio Ribeiro, and Yi Zhang. 2023. [Sparks of artificial general intelligence: Early experiments with gpt-4](https://md.research.elamur.ai/free/en/papers/bubeck-2023-sparks-artificial-general-intelligence-early/). arXiv prepr…
4. Tsun-Hin Cheung and Kin-Man Lam. 2023. Factllama: Optimizing instruction-following language models with external knowledge for automated fact-checking. In 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), pages 846–853. IEEE. [find](https://scholar.google.com/scholar?q=Tsun%2DHin+Cheung+and+Kin%2DMan+Lam%2E+2023%2E+Factllama%3A+Optimizing+instruction%2Dfollowing+language+models+with+external+knowledge+for+automated+fact%2Dchecking%2E+In+2023+Asia+Pacific+Signal+and+Information+Proc)
5. Giovanni Da San Martino, Shaden Shaar, Yifan Zhang, Seunghak Yu, Alberto Barrón-Cedeno, and Preslav Nakov. 2020. Prta: A system to support the analysis of propaganda techniques in the news. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrati… [find](https://scholar.google.com/scholar?q=Giovanni+Da+San+Martino%2C+Shaden+Shaar%2C+Yifan+Zhang%2C+Seunghak+Yu%2C+Alberto+Barr%C3%B3n%2DCedeno%2C+and+Preslav+Nakov%2E+2020%2E+Prta%3A+A+system+to+support+the+analysis+of+propaganda+techniques+in+the+news%2E+In+Proceed)
6. Giovanni Da San Martino, Seunghak Yu, Alberto Barrón-Cedeno, Rostislav Petrov, and Preslav Nakov. 2019. [Fine-grained analysis of propaganda in news articles](https://md.research.elamur.ai/free/en/papers/martino-2019-fine-grained-analysis-propaganda-news/). In Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natura…
7. Dorottya Demszky, Dana Movshovitz-Attias, Jeongwoo Ko, Alan Cowen, Gaurav Nemade, and Sujith Ravi. 2020. Goemotions: A dataset of fine-grained emotions. arXiv preprint arXiv:2005.00547. [find](https://scholar.google.com/scholar?q=Dorottya+Demszky%2C+Dana+Movshovitz%2DAttias%2C+Jeongwoo+Ko%2C+Alan+Cowen%2C+Gaurav+Nemade%2C+and+Sujith+Ravi%2E+2020%2E+Goemotions%3A+A+dataset+of+fine%2Dgrained+emotions%2E+arXiv+preprint+arXiv%3A2005%2E00547%2E)
8. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. [Bert: Pre-training of deep bidirectional transformers for language understanding](https://md.research.elamur.ai/free/en/papers/devlin-2019-bert-pre-training-deep-bidirectional/). In Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies,…
9. Yilun Du, Shuang Li, Antonio Torralba, Joshua B Tenenbaum, and Igor Mordatch. 2023. [Improving factuality and reasoning in language models through multiagent debate](https://md.research.elamur.ai/free/en/papers/du-2023-improving-factuality-reasoning-language-models/). In Forty-first International Conference on Machine Learning.
10. Chen Han, Wenzhen Zheng, and Xijin Tang. 2025. Debate-to-detect: Reformulating misinformation detection as a real-world debate with large language models. arXiv preprint arXiv:2505.18596. [find](https://scholar.google.com/scholar?q=Chen+Han%2C+Wenzhen+Zheng%2C+and+Xijin+Tang%2E+2025%2E+Debate%2Dto%2Ddetect%3A+Reformulating+misinformation+detection+as+a+real%2Dworld+debate+with+large+language+models%2E+arXiv+preprint+arXiv%3A2505%2E18596%2E)
11. Sirui Hong, Mingchen Zhuge, Jiaqi Chen, Xiawu Zheng, Yuheng Cheng, Ceyao Zhang, Jinlin Wang, Zili Wang, Steven Ka Shing Yau, Zijuan Lin, Liyang Zhou, Chenyu Ran, Lingfeng Xiao, Chenglin Wu, and Jürgen Schmidhuber. 2023. [Metagpt: Meta programming for a multi-agent collaborative framework](https://md.research.elamur.ai/free/en/papers/hong-2023-metagpt-meta-programming-multi/). In The Twel…
12. Beizhe Hu, Qiang Sheng, Juan Cao, Yuhui Shi, Yang Li, Danding Wang, and Peng Qi. 2024. Bad actor, good advisor: Exploring the role of large language models in fake news detection. Proceedings of the AAAI conference on artificial intelligence, 38(20):22105– 22113. [find](https://scholar.google.com/scholar?q=Beizhe+Hu%2C+Qiang+Sheng%2C+Juan+Cao%2C+Yuhui+Shi%2C+Yang+Li%2C+Danding+Wang%2C+and+Peng+Qi%2E+2024%2E+Bad+actor%2C+good+advisor%3A+Exploring+the+role+of+large+language+models+in+fake+news+detection%2E+Proceedings+of+the+A)
13. Yue Huang and Lichao Sun. 2023. Fakegpt: fake news generation, explanation and detection of large language models. arXiv preprint arXiv:2310.05046. — [arXiv:2310.05046](https://arxiv.org/abs/2310.05046) [find](https://scholar.google.com/scholar?q=Yue+Huang+and+Lichao+Sun%2E+2023%2E+Fakegpt%3A+fake+news+generation%2C+explanation+and+detection+of+large+language+models%2E+arXiv+preprint+arXiv%3A2310%2E05046%2E+%E2%80%94+%5BarXiv%3A2310%2E05046%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2310%2E0504)
14. Zhijing Jin, Abhinav Lalwani, Tejas Vaidhya, Xiaoyu Shen, Yiwen Ding, Zhiheng Lyu, Mrinmaya Sachan, Rada Mihalcea, and Bernhard Schoelkopf. 2022. [Logical fallacy detection](https://md.research.elamur.ai/free/en/papers/jin-2022-logical-fallacy-detection/). arXiv preprint arXiv:2202.13758.
15. Neema Kotonya and Francesca Toni. 2020. Explainable automated fact-checking: A survey. arXiv preprint arXiv:2011.03870. [find](https://scholar.google.com/scholar?q=Neema+Kotonya+and+Francesca+Toni%2E+2020%2E+Explainable+automated+fact%2Dchecking%3A+A+survey%2E+arXiv+preprint+arXiv%3A2011%2E03870%2E)
16. David M. J. Lazer, Matthew A. Baum, Yochai Benkler, Adam J. Berinsky, Kelly M. Greenhill, Filippo Menczer, Miriam J. Metzger, Brendan Nyhan, Gordon Pennycook, David Rothschild, Michael Schudson, Steven A. Sloman, Cass R. Sunstein, Emily A. Thorson, Duncan J. Watts, and Jonathan L. Zittrain. 2018. Th… [find](https://scholar.google.com/scholar?q=David+M%2E+J%2E+Lazer%2C+Matthew+A%2E+Baum%2C+Yochai+Benkler%2C+Adam+J%2E+Berinsky%2C+Kelly+M%2E+Greenhill%2C+Filippo+Menczer%2C+Miriam+J%2E+Metzger%2C+Brendan+Nyhan%2C+Gordon+Pennycook%2C+David+Rothschild%2C+Michael+Schudson%2C+Steve)
17. Stephan Lewandowsky, Ullrich KH Ecker, Colleen M Seifert, Norbert Schwarz, and John Cook. 2012. Misinformation and its correction: Continued influence and successful debiasing. Psychological science in the public interest, 13(3):106–131. [find](https://scholar.google.com/scholar?q=Stephan+Lewandowsky%2C+Ullrich+KH+Ecker%2C+Colleen+M+Seifert%2C+Norbert+Schwarz%2C+and+John+Cook%2E+2012%2E+Misinformation+and+its+correction%3A+Continued+influence+and+successful+debiasing%2E+Psychological+science+i)
18. Guohao Li, Hasan Hammoud, Hani Itani, Dmitrii Khizbullin, and Bernard Ghanem. 2023. [Camel: Communicative agents for" mind" exploration of large language model society](https://md.research.elamur.ai/free/en/papers/li-2023-camel-communicative-agents-mind/). Advances in Neural Information Processing Systems, 36:51991–52008.
19. Tian Liang, Zhiwei He, Wenxiang Jiao, Xing Wang, Yan Wang, Rui Wang, Yujiu Yang, Shuming Shi, and Zhaopeng Tu. 2024. [Encouraging divergent thinking in large language models through multi-agent debate](https://md.research.elamur.ai/free/en/papers/liang-2023-encouraging-divergent-thinking-large-language/). In Proceedings of the 2024 conference on empirical methods in natural language processing, pages 17…
20. Stephanie Lin, Jacob Hilton, and Owain Evans. 2022. [Truthfulqa: Measuring how models mimic human falsehoods](https://md.research.elamur.ai/free/en/papers/lin-2022-truthfulqa-measuring-how-models-mimic/). In Proceedings of the 60th annual meeting of the association for computational linguistics (volume 1: long papers), pages 3214–3252.
21. Yuhan Liu, Yuxuan Liu, Xiaoqing Zhang, Xiuying Chen, and Rui Yan. 2025. The truth becomes clearer through debate! multi-agent systems with large language models unmask fake news. In Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages… [find](https://scholar.google.com/scholar?q=Yuhan+Liu%2C+Yuxuan+Liu%2C+Xiaoqing+Zhang%2C+Xiuying+Chen%2C+and+Rui+Yan%2E+2025%2E+The+truth+becomes+clearer+through+debate%21+multi%2Dagent+systems+with+large+language+models+unmask+fake+news%2E+In+Proceedings+of+the)
22. Jiatong Ma, Linmei Hu, Rang Li, and Wenbo Fu. 2025. Local: Logical and causal fact-checking with llm-based multi-agents. In Proceedings of the ACM on Web Conference 2025, pages 1614–1625. [find](https://scholar.google.com/scholar?q=Jiatong+Ma%2C+Linmei+Hu%2C+Rang+Li%2C+and+Wenbo+Fu%2E+2025%2E+Local%3A+Logical+and+causal+fact%2Dchecking+with+llm%2Dbased+multi%2Dagents%2E+In+Proceedings+of+the+ACM+on+Web+Conference+2025%2C+pages+1614%E2%80%931625%2E)
23. Giovanni Da San Martino, Stefano Cresci, Alberto Barrón-Cedeño, Seunghak Yu, Roberto Di Pietro, and Preslav Nakov. 2020. A survey on computational propaganda detection. arXiv preprint arXiv:2007.08024. — [arXiv:2007.08024](https://arxiv.org/abs/2007.08024) [find](https://scholar.google.com/scholar?q=Giovanni+Da+San+Martino%2C+Stefano+Cresci%2C+Alberto+Barr%C3%B3n%2DCede%C3%B1o%2C+Seunghak+Yu%2C+Roberto+Di+Pietro%2C+and+Preslav+Nakov%2E+2020%2E+A+survey+on+computational+propaganda+detection%2E+arXiv+preprint+arXiv%3A2007%2E08024)
24. Newton Cheng, Esin Durmus, Zac Hatfield-Dodds, Scott R. Johnston, Shauna Kravec, Timothy Maxwell, Sam McCandlish, Kamal Ndousse, Oliver Rausch, Nicholas Schiefer, Da Yan, Miranda Zhang, and Ethan Perez. 2023. [Towards understanding sycophancy in language models](https://md.research.elamur.ai/free/en/papers/sharma-2023-towards-understanding-sycophancy-language-models/). arXiv preprint arXiv:2310.13548.
25. Kai Shu, Limeng Cui, Suhang Wang, Dongwon Lee, and Huan Liu. 2019. defend: Explainable fake news detection. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pages 395–405. [find](https://scholar.google.com/scholar?q=Kai+Shu%2C+Limeng+Cui%2C+Suhang+Wang%2C+Dongwon+Lee%2C+and+Huan+Liu%2E+2019%2E+defend%3A+Explainable+fake+news+detection%2E+In+Proceedings+of+the+25th+ACM+SIGKDD+international+conference+on+knowledge+discovery+%26+data)
26. Christian Stab and Iryna Gurevych. 2017. [Parsing argumentation structures in persuasive essays](https://md.research.elamur.ai/free/en/papers/stab-2017-parsing-argumentation-structures-persuasive-essays/). Computational Linguistics, 43(3):619–659.
27. James Thorne, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal. 2018. [FEVER: a large-scale dataset for fact extraction and VERification](https://md.research.elamur.ai/free/en/papers/thorne-2018-fever-large-scale-dataset-fact/). In NAACL-HLT.
28. Bahra Mohamed, Hmami Haytam, and Fennan Abdelhadi. 2021. Applying fuzzy logic and neural network in sentiment analysis for fake news detection: case of covid-19. In Combating fake news with computational intelligence techniques, pages 387–400. Springer. [find](https://scholar.google.com/scholar?q=Bahra+Mohamed%2C+Hmami+Haytam%2C+and+Fennan+Abdelhadi%2E+2021%2E+Applying+fuzzy+logic+and+neural+network+in+sentiment+analysis+for+fake+news+detection%3A+case+of+covid%2D19%2E+In+Combating+fake+news+with+computatio)
29. Herun Wan, Shangbin Feng, Zhaoxuan Tan, Heng Wang, Yulia Tsvetkov, and Minnan Luo. 2024. Dell: Generating reactions and explanations for llm-based misinformation detection. arXiv preprint arXiv:2402.10426. [find](https://scholar.google.com/scholar?q=Herun+Wan%2C+Shangbin+Feng%2C+Zhaoxuan+Tan%2C+Heng+Wang%2C+Yulia+Tsvetkov%2C+and+Minnan+Luo%2E+2024%2E+Dell%3A+Generating+reactions+and+explanations+for+llm%2Dbased+misinformation+detection%2E+arXiv+preprint+arXiv%3A2402%2E1)
30. Qiong Nan, Juan Cao, Yongchun Zhu, Yanyan Wang, and Jintao Li. 2021. Mdfend: Multi-domain fake news detection. In Proceedings of the 30th ACM international conference on information & knowledge management, pages 3343–3347. [find](https://scholar.google.com/scholar?q=Qiong+Nan%2C+Juan+Cao%2C+Yongchun+Zhu%2C+Yanyan+Wang%2C+and+Jintao+Li%2E+2021%2E+Mdfend%3A+Multi%2Ddomain+fake+news+detection%2E+In+Proceedings+of+the+30th+ACM+international+conference+on+information+%26+knowledge+manage)
31. Bo Wang, Jing Ma, Hongzhan Lin, Zhiwei Yang, Ruichao Yang, Yuan Tian, and Yi Chang. 2024. Explainable fake news detection with large language model via defense among competing wisdom. In Proceedings of the ACM Web Conference 2024, pages 2452–2463. [find](https://scholar.google.com/scholar?q=Bo+Wang%2C+Jing+Ma%2C+Hongzhan+Lin%2C+Zhiwei+Yang%2C+Ruichao+Yang%2C+Yuan+Tian%2C+and+Yi+Chang%2E+2024%2E+Explainable+fake+news+detection+with+large+language+model+via+defense+among+competing+wisdom%2E+In+Proceedings+o)
32. Silviu Oprea and Walid Magdy. 2020. [isarcasm: A dataset of intended sarcasm](https://md.research.elamur.ai/free/en/papers/oprea-2020-isarcasm-dataset-intended-sarcasm/). In Proceedings of the 58th annual meeting of the association for computational linguistics, pages 1279–1289.
33. Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, and Ryan Lowe. 2022. [Train…](https://md.research.elamur.ai/free/en/papers/ouyang-2022-training-language-models-follow-instructions/)
34. Liangming Pan, Xiaobao Wu, Xinyuan Lu, Luu Anh Tuan, William Yang Wang, Min-Yen Kan, and Preslav Nakov. 2023. [Fact-checking complex claims with program-guided reasoning](https://md.research.elamur.ai/free/en/papers/pan-2023-fact-checking-complex-claims-program/). In Proceedings of the 61st annual meeting of the association for computational linguistics (volume 1: long papers), pages 6981–700…
35. Alamgir Munir Qazi, John Philip McCrae, and Jamal A Nasir. 2025. When retrieval outperforms generation: Dense evidence retrieval for scalable fake news detection. In Proceedings of the 5th Conference on Language, Data and Knowledge, pages 255–265. [find](https://scholar.google.com/scholar?q=Alamgir+Munir+Qazi%2C+John+Philip+McCrae%2C+and+Jamal+A+Nasir%2E+2025%2E+When+retrieval+outperforms+generation%3A+Dense+evidence+retrieval+for+scalable+fake+news+detection%2E+In+Proceedings+of+the+5th+Conference+)
36. Mrinank Sharma, Meg Tong, Tomasz Korbak, David Duvenaud, Amanda Askell, Samuel R. Bowman, Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc Le, and Denny Zhou. 2022. [Chain-of-thought prompting elicits reasoning in large language models](https://md.research.elamur.ai/free/en/papers/wei-2022-chain-thought-prompting-elicits-reasoning/). Advances in neural in…
37. Jiaying Wu, Jiafeng Guo, and Bryan Hooi. 2024a. Fake news in sheep’s clothing: Robust fake news detection against llm-empowered style attacks. In Proceedings of the 30th ACM SIGKDD conference on knowledge discovery and data mining, pages 3367–3378. [find](https://scholar.google.com/scholar?q=Jiaying+Wu%2C+Jiafeng+Guo%2C+and+Bryan+Hooi%2E+2024a%2E+Fake+news+in+sheep%E2%80%99s+clothing%3A+Robust+fake+news+detection+against+llm%2Dempowered+style+attacks%2E+In+Proceedings+of+the+30th+ACM+SIGKDD+conference+on+knowl)
38. Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Beibin Li, Erkang Zhu, Li Jiang, Xiaoyun Zhang, Shaokun Zhang, Jiale Liu, Ahmed Hassan Awadallah, Ryen W White, Doug Burger, and Chi Wang. 2024b. Autogen: Enabling next-gen llm applications via multi-agent conversations. In First Conference on Languag… [find](https://scholar.google.com/scholar?q=Qingyun+Wu%2C+Gagan+Bansal%2C+Jieyu+Zhang%2C+Yiran+Wu%2C+Beibin+Li%2C+Erkang+Zhu%2C+Li+Jiang%2C+Xiaoyun+Zhang%2C+Shaokun+Zhang%2C+Jiale+Liu%2C+Ahmed+Hassan+Awadallah%2C+Ryen+W+White%2C+Doug+Burger%2C+and+Chi+Wang%2E+2024b%2E+Autog)
39. Kai Xiong, Xiao Ding, Yixin Cao, Ting Liu, and Bing Qin. 2023. [Examining inter-consistency of large language models collaboration: An in-depth analysis via debate](https://md.research.elamur.ai/free/en/papers/xiong-2023-examining-inter-consistency-large-language/). arXiv preprint arXiv:2305.11595.
40. Zhiwei Yang, Jing Ma, Hechang Chen, Hongzhan Lin, Ziyang Luo, and Yi Chang. 2022. A coarse-to-fine cascaded evidence-distillation neural network for explainable fake news detection. arXiv preprint arXiv:2209.14642. [find](https://scholar.google.com/scholar?q=Zhiwei+Yang%2C+Jing+Ma%2C+Hechang+Chen%2C+Hongzhan+Lin%2C+Ziyang+Luo%2C+and+Yi+Chang%2E+2022%2E+A+coarse%2Dto%2Dfine+cascaded+evidence%2Ddistillation+neural+network+for+explainable+fake+news+detection%2E+arXiv+preprint+arX)
41. Min-Hsuan Yeh, Ruyuan Wan, and Ting-Hao Huang. 2024. Cocolofa: A dataset of news comments with common logical fallacies written by llm-assisted crowds. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 660–677. [find](https://scholar.google.com/scholar?q=Min%2DHsuan+Yeh%2C+Ruyuan+Wan%2C+and+Ting%2DHao+Huang%2E+2024%2E+Cocolofa%3A+A+dataset+of+news+comments+with+common+logical+fallacies+written+by+llm%2Dassisted+crowds%2E+In+Proceedings+of+the+2024+Conference+on+Empiric)
42. Weihao Yu, Zihang Jiang, Yanfei Dong, and Jiashi Feng. 2020. [Reclor: A reading comprehension dataset requiring logical reasoning](https://md.research.elamur.ai/free/en/papers/yu-2020-reclor-reading-comprehension-dataset-requiring/). arXiv preprint arXiv:2002.04326.
43. Wojciech Zaremba, Ilya Sutskever, and Oriol Vinyals. 2014. [Recurrent neural network regularization](https://md.research.elamur.ai/free/en/papers/zaremba-2014-recurrent-neural-network-regularization/). arXiv preprint arXiv:1409.2329.
44. Mingqing Zhang, Haisong Gong, Qiang Liu, Shu Wu, and Liang Wang. 2024a. Breaking event rumor detection via stance-separated multi-agent debate. arXiv preprint arXiv:2412.04859. — [arXiv:2412.04859](https://arxiv.org/abs/2412.04859) [find](https://scholar.google.com/scholar?q=Mingqing+Zhang%2C+Haisong+Gong%2C+Qiang+Liu%2C+Shu+Wu%2C+and+Liang+Wang%2E+2024a%2E+Breaking+event+rumor+detection+via+stance%2Dseparated+multi%2Dagent+debate%2E+arXiv+preprint+arXiv%3A2412%2E04859%2E+%E2%80%94+%5BarXiv%3A2412%2E04859%5D%28ht)
45. Xueyao Zhang, Juan Cao, Xirong Li, Qiang Sheng, Lei Zhong, and Kai Shu. 2021. Mining dual emotion for fake news detection. In Proceedings of the web conference 2021, pages 3465–3476. [find](https://scholar.google.com/scholar?q=Xueyao+Zhang%2C+Juan+Cao%2C+Xirong+Li%2C+Qiang+Sheng%2C+Lei+Zhong%2C+and+Kai+Shu%2E+2021%2E+Mining+dual+emotion+for+fake+news+detection%2E+In+Proceedings+of+the+web+conference+2021%2C+pages+3465%E2%80%933476%2E)
46. Yuchen Zhang, Xiaoxiao Ma, Jia Wu, Jian Yang, and Hao Fan. 2024b. Heterogeneous subgraph transformer for fake news detection. In Proceedings of the ACM web conference 2024, pages 1272–1282. [find](https://scholar.google.com/scholar?q=Yuchen+Zhang%2C+Xiaoxiao+Ma%2C+Jia+Wu%2C+Jian+Yang%2C+and+Hao+Fan%2E+2024b%2E+Heterogeneous+subgraph+transformer+for+fake+news+detection%2E+In+Proceedings+of+the+ACM+web+conference+2024%2C+pages+1272%E2%80%931282%2E)

*Updated 2 November 2026*