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cs.LG2026
Improving Semantic Uncertainty Quantification in Language Model Question-Answering via Token-Level Temperature Scaling
Tom A. Lamb, Desi R. Ivanova, Philip H. S. Torr +1
Calibration is central to reliable semantic uncertainty quantification, yet prior work has largely focused on discrimination, neglecting calibration. As calibration and discriminat…
cs.LG2024
Detecting LLM Hallucination Through Layer-wise Information Deficiency: Analysis of Ambiguous Prompts and Unanswerable Questions
Hazel Kim, Tom A. Lamb, Adel Bibi +2
Large language models (LLMs) frequently generate confident yet inaccurate responses, introducing significant risks for deployment in safety-critical domains. We present a novel, te…
cs.LG2024
Focus On This, Not That! Steering LLMs with Adaptive Feature Specification
Tom A. Lamb, Adam Davies, Alasdair Paren +2
Despite the success of Instruction Tuning (IT) in training large language models (LLMs), such models often leverage spurious or biased features learnt from their training data and…