Jaeyoon Jung
2024
HerO at AVeriTeC: The Herd of Open Large Language Models for Verifying Real-World Claims
Yejun Yoon
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Jaeyoon Jung
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Seunghyun Yoon
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Kunwoo Park
Proceedings of the Seventh Fact Extraction and VERification Workshop (FEVER)
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). HerO employs multiple LLMs for each step of automated fact-checking. For evidence retrieval, a language model is used to enhance a query by generating hypothetical documents that check the veracity of a claim. We fine-tune 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.
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