6 papers
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…
Towards Label-Free Biological Reasoning Synthetic Dataset Creation via Uncertainty Filtering
Josefa Lia Stoisser, Lawrence Phillips, Aditya Misra +5
Synthetic chain-of-thought (CoT) traces are widely used to train large reasoning models (LRMs), improving generalization by providing step-level supervision. Yet most approaches re…
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…
Can Large Language Models Express Uncertainty Like Human?
Linwei Tao, Yi-Fan Yeh, Bo Kai +6
Large language models (LLMs) are increasingly used in high-stakes settings, where overconfident responses can mislead users. Reliable confidence estimation has been shown to enhanc…
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…
Universal In-Context Approximation By Prompting Fully Recurrent Models
Aleksandar Petrov, Tom A. Lamb, Alasdair Paren +2
Zero-shot and in-context learning enable solving tasks without model fine-tuning, making them essential for developing generative model solutions. Therefore, it is crucial to under…