Showing 2026Show all
2 papers · 1 filter
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…