{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/hero-at-averitec-the-herd-of-open-large","title":"HerO at AVeriTeC: The Herd of Open Large Language Models for Verifying Real-World Claims","arxiv_id":"2410.12377","date":"2024-10-16","proceeding":null,"authors":["Yejun Yoon","JaeYoon Jung","Seunghyun Yoon","Kunwoo Park"],"abstract":"To tackle the AVeriTeC shared task hosted by the FEVER-24, we introduce a system that only employs publicly available large language models (LLMs) for each step of automated fact-checking, dubbed the Herd of Open LLMs for verifying real-world claims (HerO). For evidence retrieval, a language model is used to enhance a query by generating hypothetical fact-checking documents. We prompt pretrained and fine-tuned LLMs for question generation and veracity prediction by crafting prompts with retrieved in-context samples. HerO achieved 2nd place on the leaderboard with the AVeriTeC score of 0.57, suggesting the potential of open LLMs for verifying real-world claims. For future research, we make our code publicly available at https://github.com/ssu-humane/HerO.","url_abs":"https://arxiv.org/abs/2410.12377v2","url_pdf":"https://arxiv.org/pdf/2410.12377v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"hero-at-averitec-the-herd-of-open-large","repo_url":"https://github.com/ssu-humane/hero","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"fact-checking","task_name":"Fact Checking"},{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":"question-generation","task_name":"Question Generation"},{"task_slug":"question-generation","task_name":"Question-Generation"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/fact-checking-on-averitec","task":"Fact Checking","dataset":"AVeriTeC","model":"HerO","rank_in_archive_order":1,"of":3,"metrics":{"AveriTeC":"0.57","Question + Answer score":"0.35","Question Only score":"0.48"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2410.12377","atlas_url":"https://app.syntology.ai/?focus=2410.12377","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}