mlsanityData to 14 Oct 2026

HerO at AVeriTeC: The Herd of Open Large Language Models for Verifying Real-World Claims

arXiv 2410.12377 ↗First posted 16 Oct 2024cs.CL cs.CYcited by 12

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Abstract (arXiv)

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.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 14 of its 23 references that are in the map. First posted on arXiv 16 Oct 2024.

Builds on

14 of 23 references.

References · 14

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2023GPT-4 Technical Reportcited by 18,396
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  9. 2021A Survey on Automated Fact-Checkingcited by 378
  10. 2017"Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detectioncited by 363
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  12. 2021FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured informationcited by 167
2 more
  1. 2023AVeriTeC: A Dataset for Real-world Claim Verification with Evidence from the Webcited by 144
  2. 2018The Fact Extraction and VERification (FEVER) Shared Taskcited by 108

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 7; 2026: 5.

12 papers.