6 citations · 7 across the 8 of their papers we have counts for
3 papers · 1 filter
Debate Training Reduces Reward Hacking in RLAIF
Zachary Kenton, Lili Janzer, Rory Greig +8
We demonstrate that RL finetuning an LLM using debate, a two-player adversarial game between a generator and a critic adjudicated by a weaker LLM judge, reduces reward hacking comp…
Training Large Language Models for Self-Explanation Faithfulness
Yeoktatt Cheah, María Pérez-Ortiz, Noah Y. Siegel +1
We propose a Reinforcement Learning (RL) method to directly optimize the faithfulness of self-explanations - the extent to which a model's generated reasoning accurately reflects i…
A Positive Case for Faithfulness: LLM Self-Explanations Help Predict Model Behavior
Harry Mayne, Justin Singh Kang, Dewi Gould +3
LLM self-explanations are often presented as a promising tool for AI oversight, yet their faithfulness to the model's true reasoning process is poorly understood. Existing faithful…