19 citations · 34 across the 7 of their papers we have counts for
3 papers · 1 filter
Fine-Tuning Large Language Models to Appropriately Abstain with Semantic Entropy
Benedict Aaron Tjandra, Muhammed Razzak, Jannik Kossen +2
Large Language Models (LLMs) are known to hallucinate, whereby they generate plausible but inaccurate text. This phenomenon poses significant risks in critical applications, such a…
Semantic Entropy Probes: Robust and Cheap Hallucination Detection in LLMs
Jannik Kossen, Jiatong Han, Muhammed Razzak +3
We propose semantic entropy probes (SEPs), a cheap and reliable method for uncertainty quantification in Large Language Models (LLMs). Hallucinations, which are plausible-sounding…
The Benefits and Risks of Transductive Approaches for AI Fairness
Muhammed Razzak, Andreas Kirsch, Yarin Gal
Recently, transductive learning methods, which leverage holdout sets during training, have gained popularity for their potential to improve speed, accuracy, and fairness in machine…