{"id":14885448,"url":"https://github.com/ICTMCG/LLM-for-misinformation-research","last_synced_at":"2025-09-21T08:30:39.384Z","repository":{"id":238952029,"uuid":"798041157","full_name":"ICTMCG/LLM-for-misinformation-research","owner":"ICTMCG","description":"Paper list of misinformation research using (multi-modal) large language models, i.e., (M)LLMs.","archived":false,"fork":false,"pushed_at":"2024-12-08T09:56:07.000Z","size":241,"stargazers_count":183,"open_issues_count":0,"forks_count":8,"subscribers_count":8,"default_branch":"main","last_synced_at":"2025-01-13T10:38:29.233Z","etag":null,"topics":["fact-checking","fact-verification","fake-news-detection","large-language-models","misinformation","paper-list","rumor-detection"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ICTMCG.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-05-09T01:20:59.000Z","updated_at":"2025-01-12T05:57:55.000Z","dependencies_parsed_at":"2024-05-09T04:40:39.316Z","dependency_job_id":"68a8acf8-5ded-447e-b992-09aa6c01c97f","html_url":"https://github.com/ICTMCG/LLM-for-misinformation-research","commit_stats":null,"previous_names":["ictmcg/llm-for-misinformation-research"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/ICTMCG/LLM-for-misinformation-research","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ICTMCG%2FLLM-for-misinformation-research","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ICTMCG%2FLLM-for-misinformation-research/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ICTMCG%2FLLM-for-misinformation-research/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ICTMCG%2FLLM-for-misinformation-research/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ICTMCG","download_url":"https://codeload.github.com/ICTMCG/LLM-for-misinformation-research/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ICTMCG%2FLLM-for-misinformation-research/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":276211614,"owners_count":25603989,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-09-21T02:00:07.055Z","response_time":72,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["fact-checking","fact-verification","fake-news-detection","large-language-models","misinformation","paper-list","rumor-detection"],"created_at":"2024-09-21T16:01:10.362Z","updated_at":"2025-09-21T08:30:39.117Z","avatar_url":"https://github.com/ICTMCG.png","language":null,"funding_links":[],"categories":["Others"],"sub_categories":[],"readme":"# LLM-for-misinformation-research\nA curated paper list of misinformation research using (multi-modal) large language models, i.e., (M)LLMs.\n\n## Methods for Detection and Verification\n\n### As an Information/Feature Provider, Data Generator, and Analyzer\n\n\u003e An LLM can be seen as a (sometimes not reliable) knowledge provider, an experienced expert in specific areas, and a relatively cheap data generator (compared with collecting from the real world). For example, LLMs could be a good analyzer of social commonsense/conventions.\n\n- **Cheap-fake Detection with LLM using Prompt Engineering**[[paper]](https://arxiv.org/abs/2306.02776) ![](https://img.shields.io/badge/arXiv-orange) ![](https://img.shields.io/badge/2023.06-blue)\n- **Faking Fake News for Real Fake News Detection: Propaganda-Loaded Training Data Generation**[[paper]](https://doi.org/10.18653/v1/2023.acl-long.815) ![](https://img.shields.io/badge/ACL%202023-orange) ![](https://img.shields.io/badge/2023.07-blue)\n- **Bad Actor, Good Advisor: Exploring the Role of Large Language Models in Fake News Detection**[[paper]](https://ojs.aaai.org/index.php/AAAI/article/view/30214) ![](https://img.shields.io/badge/AAAI%202024-orange) ![](https://img.shields.io/badge/2023.09-blue)\n- **Analysis of Disinformation and Fake News Detection Using Fine-Tuned Large Language Model**[[paper]](https://arxiv.org/abs/2309.04704) ![](https://img.shields.io/badge/arXiv-orange) ![](https://img.shields.io/badge/2023.09-blue)\n- **Detecting Misinformation with LLM-Predicted Credibility Signals and Weak Supervision**[[paper]](https://arxiv.org/abs/2309.07601) ![](https://img.shields.io/badge/arXiv-orange) ![](https://img.shields.io/badge/2023.09-blue)\n- **FakeGPT: Fake News Generation, Explanation and Detection of Large Language Model**[[paper]](https://arxiv.org/abs/2310.05046) ![](https://img.shields.io/badge/arXiv-orange)  ![](https://img.shields.io/badge/2023.10-blue)\n- **Fighting Fire with Fire: The Dual Role of LLMs in Crafting and Detecting Elusive Disinformation**[[paper]](https://aclanthology.org/2023.emnlp-main.883/) ![](https://img.shields.io/badge/EMNLP%202023-orange)  ![](https://img.shields.io/badge/2023.10-blue)\n- **Language Models Hallucinate, but May Excel at Fact Verification**[[paper]](https://arxiv.org/abs/2310.14564) ![](https://img.shields.io/badge/NAACL%202024-orange)  ![](https://img.shields.io/badge/2023.10-blue)\n- **Clean-label Poisoning Attack against Fake News Detection Models**[[paper]](https://doi.org/10.1109/BigData59044.2023.10386777) ![](https://img.shields.io/badge/BigData%202023-orange)  ![](https://img.shields.io/badge/2023.12-blue)\n- **Rumor Detection on Social Media with Crowd Intelligence and ChatGPT-Assisted Networks**[[paper]](https://doi.org/10.18653/v1/2023.emnlp-main.347) ![](https://img.shields.io/badge/EMNLP%202023-orange) ![](https://img.shields.io/badge/2023.12-blue)\n- **LLMs are Superior Feedback Providers: Bootstrapping Reasoning for Lie Detection with Self-Generated Feedback**[[paper]](https://tanushreebanerjee.github.io/pdfs/diplomacy_main.pdf) ![](https://img.shields.io/badge/2024.01-blue)\n- **Can Large Language Models Detect Rumors on Social Media?**[[paper]](https://arxiv.org/abs/2402.03916) ![](https://img.shields.io/badge/arXiv-orange) ![](https://img.shields.io/badge/2024.02-blue)\n- **TELLER: A Trustworthy Framework for Explainable, Generalizable and Controllable Fake News Detection**[[paper]](https://arxiv.org/abs/2402.07776) ![](https://img.shields.io/badge/ACL%202024%20Findings-orange) ![](https://img.shields.io/badge/2024.02-blue)\n- **DELL: Generating Reactions and Explanations for LLM-Based Misinformation Detection**[[paper]](https://arxiv.org/abs/2402.10426) ![](https://img.shields.io/badge/ACL%202024%20Findings-orange) ![](https://img.shields.io/badge/2024.02-blue)\n- **Enhancing large language model capabilities for rumor detection with Knowledge-Powered Prompting**[[paper]](https://doi.org/10.1016/j.engappai.2024.108259) ![](https://img.shields.io/badge/EAAI-orange) ![](https://img.shields.io/badge/2024.03-blue)\n- **An Implicit Semantic Enhanced Fine-Grained Fake News Detection Method Based on Large Language Model**[[paper]](https://dx.doi.org/10.7544/issn1000-1239.202330967) ![](https://img.shields.io/badge/JCRAD-orange) ![](https://img.shields.io/badge/2024.03-blue)\n- **RumorLLM: A Rumor Large Language Model-Based Fake-News-Detection Data-Augmentation Approach**[[paper]](https://doi.org/10.3390/app14083532) ![](https://img.shields.io/badge/Applied%20Science-orange) ![](https://img.shields.io/badge/2024.04-blue)\n- **Explainable Fake News Detection With Large Language Model via Defense Among Competing Wisdom**[[paper]](https://doi.org/10.1145/3589334.3645471) ![](https://img.shields.io/badge/WWW%202024-orange) ![](https://img.shields.io/badge/2024.05-blue)\n- **Message Injection Attack on Rumor Detection under the Black-Box Evasion Setting Using Large Language Model**[[paper]](https://doi.org/10.1145/3589334.3648139) ![](https://img.shields.io/badge/WWW%202024-orange) ![](https://img.shields.io/badge/2024.05-blue)\n- **Towards Robust Evidence-Aware Fake News Detection via Improving Semantic Perception**[[paper]](https://aclanthology.org/2024.lrec-main.1443/) ![](https://img.shields.io/badge/LREC--COLING%202024-orange) ![](https://img.shields.io/badge/2024.05-blue)\n- **Let Silence Speak: Enhancing Fake News Detection with Generated Comments from Large Language Models**[[paper]](https://arxiv.org/abs/2405.16631) ![](https://img.shields.io/badge/CIKM%202024-orange) ![](https://img.shields.io/badge/2024.05-blue)\n- **RAEmoLLM: Retrieval Augmented LLMs for Cross-Domain Misinformation Detection Using In-Context Learning based on Emotional Information**[[paper]](https://arxiv.org/abs/2406.11093) ![](https://img.shields.io/badge/arXiv-orange) ![](https://img.shields.io/badge/2024.06-blue)\n- **Adversarial Style Augmentation via Large Language Model for Robust Fake News Detection**[[paper]](https://arxiv.org/abs/2406.11260) ![](https://img.shields.io/badge/arXiv-orange) ![](https://img.shields.io/badge/2024.06-blue)\n- **Zero-Shot Fact Verification via Natural Logic and Large Language Models**[[paper]](https://openreview.net/attachment?id=SxGFTTQwCm\u0026name=pdf) ![](https://img.shields.io/badge/ARR%20June%202024-orange) ![](https://img.shields.io/badge/2024.06-blue)\n- **RAGAR, Your Falsehood Radar: RAG-Augmented Reasoning for Political Fact-Checking using Multimodal Large Language Models**[[paper]](https://openreview.net/attachment?id=TNOnM4CQsl\u0026name=pdf) ![](https://img.shields.io/badge/ARR%20June%202024-orange) ![](https://img.shields.io/badge/2024.06-blue)\n- **FramedTruth: A Frame-Based Model Utilising Large Language Models for Misinformation Detection**[[paper]](https://doi.org/10.1007/978-981-97-4982-9_11) ![](https://img.shields.io/badge/ACIIDS%202024-orange) ![](https://img.shields.io/badge/2024.07-blue)\n- **Enhancing Fake News Detection through Dataset Augmentation Using Large Language Models**[[Thesis]](https://ntnuopen.ntnu.no/ntnu-xmlui/bitstream/handle/11250/3154951/no.ntnu:inspera:187264004:35303025.pdf) ![](https://img.shields.io/badge/NTNU-Thesis-orange) ![](https://img.shields.io/badge/2024.07-blue)\n- **DAAD: Dynamic Analysis and Adaptive Discriminator for Fake News Detection** [[paper]](https://arxiv.org/abs/2408.10883) ![](https://img.shields.io/badge/arXiv-orange) ![](https://img.shields.io/badge/2024.08-blue)\n- **CoVLM: Leveraging Consensus from Vision-Language Models for Semi-supervised Multimodal Fake News Detection** [[paper]](https://openaccess.thecvf.com/content/ACCV2024/papers/Devank_CoVLM_Leveraging_Consensus_from_Vision-Language_Models_for_Semi-supervised_Multimodal_Fake_ACCV_2024_paper.pdf) ![](https://img.shields.io/badge/ACCV%202024-orange) ![](https://img.shields.io/badge/2024.12-blue)\n\n### As a Tool User\n\n\u003e Let an LLM be an agent having access to external tools like search engines, deepfake detectors, etc.\n\n- **Fact-Checking Complex Claims with Program-Guided Reasoning**[[paper]](https://doi.org/10.18653/v1/2023.acl-long.386) ![](https://img.shields.io/badge/ACL%202023-orange)  ![](https://img.shields.io/badge/2023.05-blue)\n- **Self-Checker: Plug-and-Play Modules for Fact-Checking with Large Language Models**[[paper]](https://arxiv.org/abs/2305.14623) ![](https://img.shields.io/badge/NAACL%202024%20Findings-orange) ![](https://img.shields.io/badge/2023.05-blue)\n- **FacTool: Factuality Detection in Generative AI -- A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarios**[[paper]](https://arxiv.org/abs/2307.13528) ![](https://img.shields.io/badge/arXiv-orange) ![](https://img.shields.io/badge/2023.07-blue)\n- **FactLLaMA: Optimizing Instruction-Following Language Models with External Knowledge for Automated Fact-Checking**[[paper]](https://arxiv.org/abs/2309.00240) ![](https://img.shields.io/badge/APSIPA%20ASC%202023-orange) ![](https://img.shields.io/badge/2023.09-blue)\n- **Explainable Claim Verification via Knowledge-Grounded Reasoning with Large Language Models**[[paper]](https://aclanthology.org/2023.findings-emnlp.416) ![](https://img.shields.io/badge/EMNLP%202023%20Findings-orange) ![](https://img.shields.io/badge/2023.10-blue)\n- **Language Models Hallucinate, but May Excel at Fact Verification**[[paper]](https://arxiv.org/abs/2310.14564) ![](https://img.shields.io/badge/NAACL%202024-orange)  ![](https://img.shields.io/badge/2023.10-blue)\n- **Towards LLM-based Fact Verification on News Claims with a Hierarchical Step-by-Step Prompting Method**[[paper]](https://aclanthology.org/2023.ijcnlp-main.64) ![](https://img.shields.io/badge/AACL%202023-orange) ![](https://img.shields.io/badge/2023.11-blue)\n- **Evidence-based Interpretable Open-domain Fact-checking with Large Language Models**[[paper]](https://arxiv.org/abs/2312.05834) ![](https://img.shields.io/badge/arXiv-orange) ![](https://img.shields.io/badge/2023.12-blue)\n- **TELLER: A Trustworthy Framework for Explainable, Generalizable and Controllable Fake News Detection**[[paper]](https://arxiv.org/abs/2402.07776) ![](https://img.shields.io/badge/ACL%202024%20Findings-orange) ![](https://img.shields.io/badge/2024.02-blue)\n- **LEMMA: Towards LVLM-Enhanced Multimodal Misinformation Detection with External Knowledge Augmentation**[[paper]](https://arxiv.org/abs/2402.11943) ![](https://img.shields.io/badge/arXiv-orange) ![](https://img.shields.io/badge/2024.02-blue)\n- **Can Large Language Models Detect Misinformation in Scientific News Reporting?**[[paper]](https://arxiv.org/abs/2402.14268) ![](https://img.shields.io/badge/arXiv-orange) ![](https://img.shields.io/badge/2024.02-blue)\n- **The Perils and Promises of Fact-Checking with Large Language Models**[[paper]](https://doi.org/10.3389%2Ffrai.2024.1341697) ![](https://img.shields.io/badge/Frontiers%20in%20AI-orange) ![](https://img.shields.io/badge/2024.02-blue)\n- **SNIFFER: Multimodal Large Language Model for Explainable Out-of-Context Misinformation Detection**[[paper]](https://arxiv.org/abs/2403.03170) ![](https://img.shields.io/badge/CVPR%202024-orange) ![](https://img.shields.io/badge/2024.03-blue)\n- **Re-Search for The Truth: Multi-round Retrieval-augmented Large Language Models are Strong Fake News Detectors**[[paper]](https://arxiv.org/abs/2403.09747) ![](https://img.shields.io/badge/arXiv-orange) ![](https://img.shields.io/badge/2024.03-blue)\n- **MMIDR: Teaching Large Language Model to Interpret Multimodal Misinformation via Knowledge Distillation**[[paper]](https://arxiv.org/abs/2403.14171) ![](https://img.shields.io/badge/arXiv-orange) ![](https://img.shields.io/badge/2024.03-blue)\n- **TrumorGPT: Query Optimization and Semantic Reasoning over Networks for Automated Fact-Checking**[[paper]](https://doi.org/10.1109/CISS59072.2024.10480162) ![](https://img.shields.io/badge/CISS%202024-orange) ![](https://img.shields.io/badge/2024.04-blue)\n- **Reinforcement Retrieval Leveraging Fine-grained Feedback for Fact Checking News Claims with Black-Box LLM**[[paper]](https://aclanthology.org/2024.lrec-main.1209) ![](https://img.shields.io/badge/LREC--COLING%202024-orange) ![](https://img.shields.io/badge/2024.05-blue)\n- **Large Language Model Agent for Fake News Detection**[[paper]](https://arxiv.org/abs/2405.01593) ![](https://img.shields.io/badge/arXiv-orange) ![](https://img.shields.io/badge/2024.05-blue)\n- **Argumentative Large Language Models for Explainable and Contestable Decision-Making**[[paper]](https://arxiv.org/abs/2405.02079) ![](https://img.shields.io/badge/arXiv-orange) ![](https://img.shields.io/badge/2024.05-blue)\n- **RAEmoLLM: Retrieval Augmented LLMs for Cross-Domain Misinformation Detection Using In-Context Learning based on Emotional Information**[[paper]](https://arxiv.org/abs/2406.11093) ![](https://img.shields.io/badge/arXiv-orange) ![](https://img.shields.io/badge/2024.06-blue)\n- **RAGAR, Your Falsehood Radar: RAG-Augmented Reasoning for Political Fact-Checking using Multimodal Large Language Models**[[paper]](https://openreview.net/attachment?id=TNOnM4CQsl\u0026name=pdf) ![](https://img.shields.io/badge/ARR%20June%202024-orange) ![](https://img.shields.io/badge/2024.06-blue)\n- **Multimodal Misinformation Detection using Large Vision-Language Models**[[paper]](https://arxiv.org/abs/2407.14321) ![](https://img.shields.io/badge/CIKM%202024-orange) ![](https://img.shields.io/badge/2024.07-blue)\n- **Detect, Investigate, Judge and Determine: A Novel LLM-based Framework for Few-shot Fake News Detection** [[paper]](https://arxiv.org/abs/2407.08952) ![](https://img.shields.io/badge/arXiv-orange) ![](https://img.shields.io/badge/2024.07-blue)\n- **LLM-Driven External Knowledge Integration Network for Rumor Detection**[[paper]](https://doi.org/10.1007/978-981-97-5678-0_1) ![](https://img.shields.io/badge/ICIC%202024-orange) ![](https://img.shields.io/badge/2024.08-blue)\n- **Evidence-backed Fact Checking using RAG and Few-Shot In-Context Learning with LLMs**[[paper]](https://arxiv.org/abs/2408.12060) ![](https://img.shields.io/badge/arXiv-orange) ![](https://img.shields.io/badge/2024.08-blue)\n- **Web Retrieval Agents for Evidence-Based Misinformation Detection**[[paper]](https://arxiv.org/abs/2409.00009) ![](https://img.shields.io/badge/COLM%202024-orange) ![](https://img.shields.io/badge/2024.09-blue)\n- **Real-time Fake News from Adversarial Feedback**[[paper]](https://arxiv.org/abs/2410.14651) ![](https://img.shields.io/badge/arXiv-orange) ![](https://img.shields.io/badge/2024.10-blue)\n- **Resolving Unseen Rumors with Retrieval-Augmented Large Language Models**[[paper]](https://doi.org/10.1007/978-981-97-9440-9_25) ![](https://img.shields.io/badge/NLPCC%202024-orange) ![](https://img.shields.io/badge/2024.11-blue)\n- **Do not wait: Preemptive rumor detection with cooperative LLMs and accessible social context**[[paper]](https://doi.org/10.1016/j.ipm.2024.103995) ![](https://img.shields.io/badge/IPM-orange) ![](https://img.shields.io/badge/2024.12-blue)\n\n### As a Decision Maker/Explainer\n\n\u003e An LLM can directly output the final prediction and (optional) explanations.\n\n- **A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity**[[paper]](https://aclanthology.org/2023.ijcnlp-main.45/) ![](https://img.shields.io/badge/AACL%202023-orange)  ![](https://img.shields.io/badge/2023.02-blue)\n- **Large Language Models Can Rate News Outlet Credibility**[[paper]](https://arxiv.org/abs/2304.00228) ![](https://img.shields.io/badge/arXiv-orange)  ![](https://img.shields.io/badge/2023.04-blue)\n- **Fact-Checking Complex Claims with Program-Guided Reasoning**[[paper]](https://doi.org/10.18653/v1/2023.acl-long.386) ![](https://img.shields.io/badge/ACL%202023-orange)  ![](https://img.shields.io/badge/2023.05-blue)\n- **Towards Reliable Misinformation Mitigation: Generalization, Uncertainty, and GPT-4**[[paper]](https://arxiv.org/abs/2305.14928) ![](https://img.shields.io/badge/arXiv-orange)  ![](https://img.shields.io/badge/2023.05-blue)\n- **Self-Checker: Plug-and-Play Modules for Fact-Checking with Large Language Models**[[paper]](https://arxiv.org/abs/2305.14623) ![](https://img.shields.io/badge/NAACL%202024%20Findings-orange) ![](https://img.shields.io/badge/2023.05-blue)\n- **News Verifiers Showdown: A Comparative Performance Evaluation of ChatGPT 3.5, ChatGPT 4.0, Bing AI, and Bard in News Fact-Checking**[[paper]](https://arxiv.org/abs/2306.17176) ![](https://img.shields.io/badge/arXiv-orange)  ![](https://img.shields.io/badge/2023.06-blue)\n- **Analysis of Disinformation and Fake News Detection Using Fine-Tuned Large Language Model**[[paper]](https://arxiv.org/abs/2309.04704) ![](https://img.shields.io/badge/arXiv-orange) ![](https://img.shields.io/badge/2023.09-blue)\n- **Explainable Claim Verification via Knowledge-Grounded Reasoning withLarge Language Models**[[paper]](https://aclanthology.org/2023.findings-emnlp.416) ![](https://img.shields.io/badge/EMNLP%202023%20Findings-orange) ![](https://img.shields.io/badge/2023.10-blue)\n- **Language Models Hallucinate, but May Excel at Fact Verification**[[paper]](https://arxiv.org/abs/2310.14564) ![](https://img.shields.io/badge/NAACL%202024-orange)  ![](https://img.shields.io/badge/2023.10-blue)\n- **FakeGPT: Fake News Generation, Explanation and Detection of Large Language Model**[[paper]](https://arxiv.org/abs/2310.05046) ![](https://img.shields.io/badge/arXiv-orange)  ![](https://img.shields.io/badge/2023.10-blue)\n- **Can Large Language Models Understand Content and Propagation for Misinformation Detection: An Empirical Study**[[paper]](https://arxiv.org/abs/2311.12699) ![](https://img.shields.io/badge/arXiv-orange)  ![](https://img.shields.io/badge/2023.11-blue)\n- **Are Large Language Models Good Fact Checkers: A Preliminary Study**[[paper]](https://arxiv.org/abs/2311.17355) ![](https://img.shields.io/badge/arXiv-orange) ![](https://img.shields.io/badge/2023.11-blue)\n- **A Revisit of Fake News Dataset with Augmented Fact-checking by ChatGPT**[[paper]](https://arxiv.org/abs/2312.11870) ![](https://img.shields.io/badge/arXiv-orange)  ![](https://img.shields.io/badge/2023.12-blue)\n- **Can Large Language Models Detect Rumors on Social Media?**[[paper]](https://arxiv.org/abs/2402.03916) ![](https://img.shields.io/badge/arXiv-orange) ![](https://img.shields.io/badge/2024.02-blue)\n- **DELL: Generating Reactions and Explanations for LLM-Based Misinformation Detection**[[paper]](https://arxiv.org/abs/2402.10426) ![](https://img.shields.io/badge/ACL%202024%20Findings-orange) ![](https://img.shields.io/badge/2024.02-blue)\n- **Assessing the Reasoning Abilities of ChatGPT in the Context of Claim Verification**[[paper]](https://arxiv.org/abs/2402.10735) ![](https://img.shields.io/badge/arXiv-orange) ![](https://img.shields.io/badge/2024.02-blue)\n- **LEMMA: Towards LVLM-Enhanced Multimodal Misinformation Detection with External Knowledge Augmentation**[[paper]](https://arxiv.org/abs/2402.11943) ![](https://img.shields.io/badge/arXiv-orange) ![](https://img.shields.io/badge/2024.02-blue)\n- **SoMeLVLM: A Large Vision Language Model for Social Media Processing**[[paper]](https://arxiv.org/abs/2402.13022)[[project]](https://somelvlm.github.io/) ![](https://img.shields.io/badge/ACL%202024-orange) ![](https://img.shields.io/badge/2024.02-blue)\n- **Can Large Language Models Detect Misinformation in Scientific News Reporting?**[[paper]](https://arxiv.org/abs/2402.14268) ![](https://img.shields.io/badge/arXiv-orange) ![](https://img.shields.io/badge/2024.02-blue)\n- **The Perils and Promises of Fact-Checking with Large Language Models**[[paper]](https://doi.org/10.3389%2Ffrai.2024.1341697) ![](https://img.shields.io/badge/Frontiers%20in%20AI-orange) ![](https://img.shields.io/badge/2024.02-blue)\n- **Potential of Large Language Models as Tools Against Medical Disinformation**[[paper]](https://doi.org/10.1001/jamainternmed.2024.0020) ![](https://img.shields.io/badge/JAMA%20Intern%20Med-orange) ![](https://img.shields.io/badge/2024.02-blue)\n- **FakeNewsGPT4: Advancing Multimodal Fake News Detection through Knowledge-Augmented LVLMs**[[paper]](https://arxiv.org/abs/2403.01988) ![](https://img.shields.io/badge/arXiv-orange) ![](https://img.shields.io/badge/2024.03-blue)\n- **SNIFFER: Multimodal Large Language Model for Explainable Out-of-Context Misinformation Detection**[[paper]](https://arxiv.org/abs/2403.03170) ![](https://img.shields.io/badge/CVPR%202024-orange) ![](https://img.shields.io/badge/2024.03-blue)\n- **Multimodal Large Language Models to Support Real-World Fact-Checking**[[paper]](https://arxiv.org/abs/2403.03627) ![](https://img.shields.io/badge/arXiv-orange) ![](https://img.shields.io/badge/2024.03-blue)\n- **MMIDR: Teaching Large Language Model to Interpret Multimodal Misinformation via Knowledge Distillation**[[paper]](https://arxiv.org/abs/2403.14171) ![](https://img.shields.io/badge/arXiv-orange) ![](https://img.shields.io/badge/2024.03-blue)\n- **An Implicit Semantic Enhanced Fine-Grained Fake News Detection Method Based on Large Language Model**[[paper]](https://dx.doi.org/10.7544/issn1000-1239.202330967) ![](https://img.shields.io/badge/JCRAD-orange) ![](https://img.shields.io/badge/2024.03-blue)\n- **Explaining Misinformation Detection Using Large Language Models**[[paper]](https://doi.org/10.3390/electronics13091673) ![](https://img.shields.io/badge/Electronics-orange) ![](https://img.shields.io/badge/2024.04-blue)\n- **Rumour Evaluation with Very Large Language Models**[[paper]](https://arxiv.org/abs/2404.16859) ![](https://img.shields.io/badge/arXiv-orange) ![](https://img.shields.io/badge/2024.04-blue)\n- **Argumentative Large Language Models for Explainable and Contestable Decision-Making**[[paper]](https://arxiv.org/abs/2405.02079) ![](https://img.shields.io/badge/arXiv-orange) ![](https://img.shields.io/badge/2024.05-blue)\n- **Exploring the Potential of the Large Language Models (LLMs) in Identifying Misleading News Headlines**[[paper]](https://arxiv.org/abs/2405.03153) ![](https://img.shields.io/badge/arXiv-orange) ![](https://img.shields.io/badge/2024.05-blue)\n- **Tell Me Why: Explainable Public Health Fact-Checking with Large Language Models**[[paper]](https://arxiv.org/abs/2405.09454) ![](https://img.shields.io/badge/arXiv-orange) ![](https://img.shields.io/badge/2024.05-blue)\n- **Mining the Explainability and Generalization: Fact Verification Based on Self-Instruction**[[paper]](https://arxiv.org/abs/2405.12579) ![](https://img.shields.io/badge/arXiv-orange) ![](https://img.shields.io/badge/2024.05-blue)\n- **Reinforcement Tuning for Detecting Stances and Debunking Rumors Jointly with Large Language Models**[[paper]](https://arxiv.org/abs/2406.02143) ![](https://img.shields.io/badge/ACL%202024%20Findings-orange) ![](https://img.shields.io/badge/2024.06-blue)\n- **RAEmoLLM: Retrieval Augmented LLMs for Cross-Domain Misinformation Detection Using In-Context Learning based on Emotional Information**[[paper]](https://arxiv.org/abs/2406.11093) ![](https://img.shields.io/badge/arXiv-orange) ![](https://img.shields.io/badge/2024.06-blue)\n- **RAGAR, Your Falsehood Radar: RAG-Augmented Reasoning for Political Fact-Checking using Multimodal Large Language Models**[[paper]](https://openreview.net/attachment?id=TNOnM4CQsl\u0026name=pdf) ![](https://img.shields.io/badge/ARR%20June%202024-orange) ![](https://img.shields.io/badge/2024.06-blue)\n- **Multilingual Fact-Checking using LLM**[[paper]](https://openreview.net/attachment?id=2KtL13evFm\u0026name=pdf) ![](https://img.shields.io/badge/ARR%20June%202024-orange) ![](https://img.shields.io/badge/2024.06-blue)\n- **Multimodal Misinformation Detection using Large Vision-Language Models**[[paper]](https://arxiv.org/abs/2407.14321) ![](https://img.shields.io/badge/CIKM%202024-orange) ![](https://img.shields.io/badge/2024.07-blue)\n- **Detect, Investigate, Judge and Determine: A Novel LLM-based Framework for Few-shot Fake News Detection** [[paper]](https://arxiv.org/abs/2407.08952) ![](https://img.shields.io/badge/arXiv-orange) ![](https://img.shields.io/badge/2024.07-blue)\n- **Large Visual-Language Models Are Also Good Classifiers: A Study of In-Context Multimodal Fake News Detection** [[paper]](https://arxiv.org/abs/2407.12879) ![](https://img.shields.io/badge/arXiv-orange) ![](https://img.shields.io/badge/2024.07-blue)\n- **Silver Lining in the Fake News Cloud: Can Large Language Models Help Detect Misinformation?**[[paper]](https://doi.org/10.1109/TAI.2024.3440248) ![](https://img.shields.io/badge/IEEE%20TAI%202024-orange) ![](https://img.shields.io/badge/2024.08-blue)\n- **Evidence-backed Fact Checking using RAG and Few-Shot In-Context Learning with LLMs**[[paper]](https://arxiv.org/abs/2408.12060) ![](https://img.shields.io/badge/arXiv-orange) ![](https://img.shields.io/badge/2024.08-blue)\n- **CoVLM: Leveraging Consensus from Vision-Language Models for Semi-supervised Multimodal Fake News Detection** [[paper]](https://openaccess.thecvf.com/content/ACCV2024/papers/Devank_CoVLM_Leveraging_Consensus_from_Vision-Language_Models_for_Semi-supervised_Multimodal_Fake_ACCV_2024_paper.pdf) ![](https://img.shields.io/badge/ACCV%202024-orange) ![](https://img.shields.io/badge/2024.12-blue)\n\n## Claim Matching and Check-worthy Claim Detection\n- **Are Large Language Models Good Fact Checkers: A Preliminary Study**[[paper]](https://arxiv.org/abs/2311.17355) ![](https://img.shields.io/badge/arXiv-orange) ![](https://img.shields.io/badge/2023.11-blue)\n- **Claim Check-Worthiness Detection: How Well do LLMs Grasp Annotation Guidelines?**[[paper]](https://arxiv.org/abs/2404.12174) ![](https://img.shields.io/badge/arXiv-orange) ![](https://img.shields.io/badge/2024.04-blue)\n- **Automated Claim Matching with Large Language Models: Empowering Fact-Checkers in the Fight Against Misinformation**[[paper]](https://doi.org/10.1145/3589335.3651910) ![](https://img.shields.io/badge/WWW%202024%20Workshops-orange) ![](https://img.shields.io/badge/2024.05-blue)\n- **SynDy: Synthetic Dynamic Dataset Generation Framework for Misinformation Tasks**[[paper]](https://arxiv.org/abs/2405.10700) ![](https://img.shields.io/badge/SIGIR%202024-orange) ![](https://img.shields.io/badge/2024.05-blue)\n\n## Post-hoc Explanation Generation\n- **Are Large Language Models Good Fact Checkers: A Preliminary Study**[[paper]](https://arxiv.org/abs/2311.17355) ![](https://img.shields.io/badge/arXiv-orange) ![](https://img.shields.io/badge/2023.11-blue)\n- **JustiLM: Few-shot Justification Generation for Explainable Fact-Checking of Real-world Claims**[[paper]](https://arxiv.org/abs/2401.08026) ![](https://img.shields.io/badge/TACL-orange) ![](https://img.shields.io/badge/2024.01-blue)\n- **Can LLMs Produce Faithful Explanations For Fact-checking? Towards Faithful Explainable Fact-Checking via Multi-Agent Debate**[[paper]](https://arxiv.org/abs/2402.07401) ![](https://img.shields.io/badge/arXiv-orange) ![](https://img.shields.io/badge/2024.02-blue)\n\n## Other Tasks\n- **[Fake News Propagation Simulation] From Skepticism to Acceptance: Simulating the Attitude Dynamics Toward Fake News**[[paper]](https://arxiv.org/abs/2403.09498) ![](https://img.shields.io/badge/IJCAI%202024-orange) ![](https://img.shields.io/badge/2024.03-blue)\n- **[Misinformation Correction] Correcting Misinformation on Social Media with A Large Language Model**[[paper]](https://arxiv.org/abs/2403.11169) ![](https://img.shields.io/badge/arXiv-orange) ![](https://img.shields.io/badge/2024.03-blue)\n- **[Fake News Data Annoatation] Enhancing Text Classification through LLM-Driven Active Learning and Human Annotation**[[paper]](https://aclanthology.org/2024.law-1.10/) ![](https://img.shields.io/badge/LAW%202024-orange) ![](https://img.shields.io/badge/2024.03-blue)\n- **[Assisting Human Fact-Checking] On the Role of Large Language Models in Crowdsourcing Misinformation Assessment**[[paper]](https://doi.org/10.1609/icwsm.v18i1.31417) ![](https://img.shields.io/badge/ICWSM%202024-orange) ![](https://img.shields.io/badge/2024.05-blue)\n- **[Attacking Misinformation Detection] Fake News in Sheep's Clothing: Robust Fake News Detection Against LLM-Empowered Style Attacks**[[paper]](https://doi.org/10.1145/3637528.3671977) ![](https://img.shields.io/badge/KDD%202024-orange) ![](https://img.shields.io/badge/2024.08-blue)\n- **[Attacking Misinformation Detection] Attacking Misinformation Detection Using Adversarial Examples Generated by Language Models**[[paper]](https://arxiv.org/abs/2410.20940) ![](https://img.shields.io/badge/arXiv-orange) ![](https://img.shields.io/badge/2024.10-blue)\n\n## Tutorials \u0026 Surveys \u0026 Position Papers\n- **Preventing and Detecting Misinformation Generated by Large Language Models**: A tutorial about prevention and detection techniques of LLM-generated misinformation, including an introduction of recent advances of LLM-based misinformation detection. [[Webpage]](https://sigir24-llm-misinformation.github.io/) [[Slides]](https://sigir24-llm-misinformation.github.io/slides/SIGIR%202024%20Tutorial-all-version5.pdf) ![](https://img.shields.io/badge/SIGIR%202024-orange) ![](https://img.shields.io/badge/2024.07-blue)\n- **Large-Language-Model-Powered Agent-Based Framework for Misinformation and Disinformation Research: Opportunities and Open Challenges**: A research framework to generate customized agent-based social networks for disinformation simulations that would enable understanding and evaluating the phenomena whilst discussing open challenges.[[paper]](https://arxiv.org/abs/2310.07545) ![](https://img.shields.io/badge/IEEE%20S\u0026P-orange) ![](https://img.shields.io/badge/2023.10-blue)\n- **Combating Misinformation in the Age of LLMs: Opportunities and Challenges**: A survey of the opportunities (can we utilize LLMs to combat misinformation) and challenges (how to combat LLM-generated misinformation) of combating misinformation in the age of LLMs. [[Project Webpage]](https://llm-misinformation.github.io/)[[paper]](https://arxiv.org/abs/2311.05656) ![](https://img.shields.io/badge/AI%20Magazine-orange) ![](https://img.shields.io/badge/2023.11-blue)\n\n## Resources\n- [**ARG**](https://github.com/ICTMCG/ARG): A Chinese \u0026 English fake news detection dataset with adding rationales generated by GPT-3.5-Turbo.\n- [**MM-Soc**](https://arxiv.org/abs/2402.14154): A benchmark for multimodal language models in social media platforms, containing a misinformation detection task.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FICTMCG%2FLLM-for-misinformation-research","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FICTMCG%2FLLM-for-misinformation-research","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FICTMCG%2FLLM-for-misinformation-research/lists"}