5 papers · 1 filter
A Reality Check of Inference-Time Context Augmentation Methods
Haeun Yu, Geert Heyman, Pepa Atanasova +1
Large language models (LLMs) often fail to utilise the context given in the input, resulting in hallucinations or a fallback to parametric knowledge. Context augmentation methods a…
The Reliance Hypothesis: Chain-of-Thought Faithfulness Rises as Language Resource Level Falls
Lucas Resck, Jingyi Sun, Qianli Wang +2
Chain-of-Thought (CoT) faithfulness matters for monitoring, explaining and safely deploying large language models, yet it has been studied mostly in English. Languages differ in le…
Explaining the Roles of Experts in Mixture-of-Experts LLMs for Personalization
Sekh Mainul Islam, Pepa Atanasova, Isabelle Augenstein
Personalization of Large Language Models (LLMs) is increasingly important for user-centric applications, yet prior methods mainly adapt dense LLMs, whose polysemantic neurons, each…
Claim Framing as a Driver of LLM Sycophancy
Jean Seo, Pepa Atanasova, Isabelle Augenstein
Sycophancy in Large Language Models, the tendency to align responses with user preferences over ground truth, has largely been studied using questions without a clear ground truth…
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…