Papers › InFact: A Strong Baseline for Automated Fact-Checking
InFact: A Strong Baseline for Automated Fact-Checking
Mark Rothermel, Tobias Braun, Marcus Rohrbach, Anna Rohrbach
The spread of disinformation poses a global threat to democratic societies, necessitating robust and scalable Automated Fact-Checking (AFC) systems. The AVeriTeC Shared Task Challenge 2024 offers a realistic benchmark for text-based fact-checking methods. This paper presents Information-Retrieving Fact-Checker (InFact), an LLM-based approach that breaks down the task of claim verification into a 6-stage process, including evidence retrieval. When using GPT-4o as the backbone, InFact achieves an AVeriTeC score of 63% on the test set, outperforming all other 20 teams competing in the challenge, and establishing a new strong baseline for future text-only AFC systems. Qualitative analysis of mislabeled instances reveals that InFact often yields a more accurate conclusion than AVeriTeC’s human-annotated ground truth.
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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 | InFact | AveriTeC | 0.63 | #3 of 3 | Archive leaderboard | report |
| Fact Checking | AVeriTeC | InFact | Question + Answer score | 0.34 | #3 of 3 | Archive leaderboard | report |
| Fact Checking | AVeriTeC | InFact | Question Only score | 0.45 | #3 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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