7 papers
Investigating the Interplay between Contextual and Parametric Chain-of-Thought Faithfulness under Optimization
Jingyi Sun, Qianli Wang, Pepa Atanasova +2
Chain-of-Thought (CoT) faithfulness, i.e., whether CoTs genuinely reflect large language models' (LLM) underlying behavior, is typically evaluated with metrics under two disjoint p…
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,…
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…
Show me the evidence: Evaluating the role of evidence and natural language explanations in AI-supported fact-checking
Greta Warren, Jingyi Sun, Irina Shklovski +1
Although much research has focused on AI explanations to support decisions in complex information-seeking tasks such as fact-checking, the role of evidence is surprisingly under-re…
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…
Graph-Guided Textual Explanation Generation Framework
Shuzhou Yuan, Jingyi Sun, Ran Zhang +4
Natural language explanations (NLEs) are commonly used to provide plausible free-text explanations of a model's reasoning about its predictions. However, recent work has questioned…