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Yoon Kim

ICLR 2026×4ACL 2026×3可解释性强化学习与后训练评测与基准长上下文与架构推理
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0 一作 · 7 篇 · ICLR 2026 / ACL 2026

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2026 顶会论文

7
作者位次 #5/5ACL 2026Poster· Natural Language Processing

Implicit Representations of Grammaticality in Language Models

Yingshan Susan Wang · Linlu Qiu · Zhaofeng Wu · Roger P. Levy · Yoon Kim

摘要

Grammaticality and likelihood are distinct notions in human language. Pretrained language models (LMs), which are probabilistic models of language fitted to maximize corpus likelihood, generate grammatically well-formed text and discriminate well between grammatical and ungrammatical sentences in...

数据与预训练可解释性评测与基准OpenReview 项目页
作者位次 #4/8ACL 2026Poster· ACL Industry Track

LLM as a Risk Manager: LLM Semantic Filtering for Lead–Lag Trading in Prediction Markets

Sumin Kim · Minjae Kim · Jihoon Kwon · Yoon Kim · Oscar Levy · Alejandro Lopez-Lira · Yongjae Lee · CHANYEOL CHOI

摘要

Prediction markets provide a unique setting where event-level time series are directly tied to natural-language descriptions, yet discovering robust lead–lag relationships remains challenging due to spurious statistical correlations. We propose a hybrid two-stage causal screener to address this c...

可解释性评测与基准理论与学习动力学OpenReview 项目页
作者位次 #7/8ACL 2026Findings· Findings of ACL

Parallel- SFT : Improving Zero-Shot Cross-Programming-Language Transfer for Code RL

Zhaofeng Wu · Shiqi Wang · Boya Peng · Anuj Kumar Goyal · Melanie Kambadur · Sebastian Ruder · Yoon Kim · Chloe Bi

摘要

Modern language models demonstrate impressive coding capabilities in common programming languages (PLs), such as C++ and Python, but their performance in lower-resource PLs is often limited by training data availability. In principle, however, most programming skills are universal across PLs, so...

代码可解释性强化学习与后训练OpenReview 项目页

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