9 citations · 27 across the 8 of their papers we have counts for
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cs.CL2024★ 1 cited
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…
cs.CL2024
Bayesian Preference Elicitation with Language Models
Kunal Handa, Yarin Gal, Ellie Pavlick +4
Aligning AI systems to users' interests requires understanding and incorporating humans' complex values and preferences. Recently, language models (LMs) have been used to gather in…