Researchers have developed a new framework called Fact-Ablated Evaluation (FAE) to assess how well Large Language Models (LLMs) utilize provided evidence for fact-checking. The study found that current LLMs often rely more on their internal knowledge than the given evidence. To address this, a training framework named REAL (Rigorous Evidence Ablation Learning) was proposed, which uses counterfactual evidence supervision to encourage models to be more dependent on the provided evidence for their veracity judgments. AI
IMPACT This research could lead to more reliable fact-checking LLMs that are less prone to hallucination and better grounded in provided evidence.
RANK_REASON The cluster contains an academic paper detailing a new evaluation framework and training method for LLMs.
Read on arXiv cs.IR (Information Retrieval) →
- arXiv
- Fact-Ablated Evaluation (FAE)
- Hugging Face
- Large Language Models (LLMs)
- REAL (Rigorous Evidence Ablation Learning)
- alphaXiv
- CatalyzeX
- CORE Recommender
- DagsHub
- Gotit.pub
- Influence Flower
- ScienceCast
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