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20242026
most citedDeep Bayesian Active Learning for Preference Modeling in Large Language Models

1 citations · 1 across the 6 of their papers we have counts for

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cs.LG2026

Q-based Variational Inverse Reinforcement Learning

Ondrej Bajgar, Peter Tisnikar, Alessandro Abate +2

The development of safe and beneficial AI requires that systems can learn and act in accordance with human preferences. However, explicitly specifying these preferences by hand is…

cs.LG2026

Amortising Bayesian Experimental Design for Sequential Information Gathering in LLMs

Jakob Hartmann, James Harvey, Jhonathan Navott +5

Large language models (LLMs) exhibit strong reasoning and world-knowledge capabilities, yet often struggle to gather information effectively across the multi-turn interactions requ…