Papers with LLM factuality
LEAF: Learning and Evaluation Augmented by Fact-Checking to Improve Factualness in Large Language Models (2025.emnlp-industry)
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| Challenge: | Large language models (LLMs) struggle with factual accuracy in knowledge-intensive domains like healthcare. |
| Approach: | They propose a framework for improving LLM factuality in medical question answering . RAFE, Fact-Check-then-RAG and Learning from Fact Check are components . |
| Outcome: | Experimental results show that LEAF outperforms Factcheck-GPT in detecting inaccuracies and corrects errors without labeling . the framework provides a scalable solution for industrial applications requiring high factuality scores. |
When Benchmarks Age: Temporal Misalignment through Large Language Model Factuality Evaluation (2026.eacl-short)
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| Challenge: | Existing studies on LLM factuality evaluation have not investigated the reliability of static evaluation benchmarks. |
| Approach: | They examine five popular factuality benchmarks and eight LLMs released over different years to assess their reliability. |
| Outcome: | The proposed method compared five popular factuality benchmarks and eight LLMs released over different years. |
How Does Response Length Affect Long-Form Factuality (2025.findings-acl)
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| Challenge: | Despite growing attention to LLM factuality, the effect of response length on factual accuracy remains underexplored. |
| Approach: | They propose an automatic and bi-level long-form factuality evaluation framework which achieves high agreement with human annotations while being cost-effective. |
| Outcome: | The proposed framework achieves high agreement with human annotations while being cost-effective. |