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Thomas L. Griffiths

ACL 2026×2评测与基准对齐与偏好强化学习与后训练可解释性社会与人本
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0 一作 · 2 篇 · ACL 2026

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2026 顶会论文

2
作者位次 #4/5ACL 2026Findings· Findings of ACL

RLHS : Mitigating Misalignment in RLHF with Hindsight Simulation

Kaiqu Liang · Haimin Hu · Ryan Liu · Thomas L. Griffiths · Jaime Fernández Fisac

摘要

While Reinforcement Learning from Human Feedback (RLHF) has shown promise in aligning generative AI, we present empirical evidence that it can also cause severe, systematic misalignment. We hypothesize that this stems from evaluator feedback depending on downstream outcome predictions (foresight)...

对齐与偏好强化学习与后训练评测与基准OpenReview 项目页
作者位次 #7/8ACL 2026Findings· Findings of ACL

Localized Cultural Knowledge is Conserved and Controllable in Large Language Models

Veniamin Veselovsky · Berke Argın · Benedikt Stroebl · Chris Wendler · Robert West · James Evans · Thomas L. Griffiths · Arvind Narayanan

摘要

Large language models (LLMs), like human language learners, show patterns influenced by their dominant training language. Just as humans display language patterns influenced by their native tongue (semantic accents) when learning new languages, LLMs often default to English-centric responses even...

可解释性社会与人本评测与基准OpenReview 项目页

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