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cs.CL2026
Teaching Language Models to Check Grounded Claim Factuality with Human Test-Taking Strategies
Yuxuan Ye, Raul Santos-Rodriguez, Edwin Simpson
Grounded claim factuality checking is important for large language model (LLM) applications such as retrieval-augmented generation, as it helps users assess the correctness of gene…
cs.CL2026
Optimising Factual Consistency in Summarisation via Preference Learning from Multiple Imperfect Metrics
Yuxuan Ye, Raul Santos-Rodriguez, Edwin Simpson
Reinforcement learning with evaluation metrics as rewards is widely used to enhance specific capabilities of language models. However, for tasks such as factually consistent summar…
cs.CL2024
Using Similarity to Evaluate Factual Consistency in Summaries
Yuxuan Ye, Edwin Simpson, Raul Santos Rodriguez
Cutting-edge abstractive summarisers generate fluent summaries, but the factuality of the generated text is not guaranteed. Early summary factuality evaluation metrics are usually…