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Jey Han Lau

AwardACL 2026×2可解释性对齐与偏好强化学习与后训练评测与基准检索与知识
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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 #4/7ACL 2026Poster· Natural Language Processing

Controlling Distributional Bias in Multi-Round LLM Generation via KL -Optimized Fine-Tuning

Yanbei Jiang · Amr Keleg · Ryandito Diandaru · Jey Han Lau · Lea Frermann · Biaoyan Fang · Fajri Koto

摘要

While the real world is inherently stochastic, Large Language Models (LLMs) are predominantly evaluated on single-round inference against fixed ground truths. In this work, we shift the lens to distribution alignment: assessing whether LLMs, when prompted repeatedly, can generate outputs that adh...

对齐与偏好可解释性强化学习与后训练OpenReview 项目页
作者位次 #2/3ACL 2026OralOutstanding Paper· Natural Language Processing

CIG : Measuring Conversational Information Gain in Deliberative Dialogues with Semantic Memory Dynamics

Ming-Bin Chen · Jey Han Lau · Lea Frermann

摘要

Measuring the quality of public deliberation requires evaluating not only civility or argument structure, but also the informational progress of a conversation. We introduce a framework for Conversational Information Gain (CIG) that evaluates each utterance in terms of how it advances collective...

评测与基准可解释性检索与知识OpenReview 项目页

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