Papers › HerO at AVeriTeC: The Herd of Open Large Language Models for Verifying Real-World Claims
HerO at AVeriTeC: The Herd of Open Large Language Models for Verifying Real-World Claims
Yejun Yoon, JaeYoon Jung, Seunghyun Yoon, Kunwoo Park
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.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Fact Checking | AVeriTeC | HerO | AveriTeC | 0.57 | #1 of 3 | Archive leaderboard | report |
| Fact Checking | AVeriTeC | HerO | Question + Answer score | 0.35 | #1 of 3 | Archive leaderboard | report |
| Fact Checking | AVeriTeC | HerO | Question Only score | 0.48 | #1 of 3 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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