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cs.CL2025
Spark-Prover-X1: Formal Theorem Proving Through Diverse Data Training
Xinyuan Zhou, Yi Lei, Xiaoyu Zhou +7
Large Language Models (LLMs) have shown significant promise in automated theorem proving, yet progress is often constrained by the scarcity of diverse and high-quality formal langu…
cs.CL2025
Evaluation Framework for Highlight Explanations of Context Utilisation in Language Models
Jingyi Sun, Pepa Atanasova, Sagnik Ray Choudhury +2
Context utilisation, the ability of Language Models (LMs) to incorporate relevant information from the provided context when generating responses, remains largely opaque to users,…
cs.CL2025
Explaining Sources of Uncertainty in Automated Fact-Checking
Jingyi Sun, Greta Warren, Irina Shklovski +1
Understanding sources of a model's uncertainty regarding its predictions is crucial for effective human-AI collaboration. Prior work proposes using numerical uncertainty or hedges…