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Ching-Yun Ko

ACL 2026×2NeurIPS 2026可解释性安全检索与知识社会与人本理论与学习动力学
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1 一作 · 3 篇 · ACL 2026 / NeurIPS 2026

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2026 顶会论文

3
一作ACL 2026Poster· Natural Language Processing

I m R easoner: Improving Memory-based Language Models for Reasoning-in-a-Haystack Tasks

Ching-Yun Ko · Payel Das · Sihui Dai · Georgios Kollias · Subhajit Chaudhury · Aurelie C. Lozano · Pin-Yu Chen

摘要

Reasoning over long contexts remains a major challenge for language models, particularly when solving tasks that require integrating multiple facts in sequence or generalizing to new distributions. We argue that this difficulty stems from a lack of structural inductive bias. Recently, alternative...

检索与知识社会与人本理论与学习动力学OpenReview 项目页
作者位次 #4/13ACL 2026Workshop· ACL System Demonstrations

AI Steerability 360: A Toolkit for Steering Large Language Models

Erik Miehling · Karthikeyan Natesan Ramamurthy · Praveen Venkateswaran · Ching-Yun Ko · Pierre Dognin · Moninder Singh · Tejaswini Pedapati · Avinash Balakrishnan · Matthew Riemer · Dennis Wei · Inge Vejsbjerg · Elizabeth M. Daly · Kush R. Varshney

摘要

The AI Steerability 360 toolkit is an extensible, open-source Python library for steering LLMs. Steering abstractions are designed around four model control surfaces: input (modification of the prompt), structural (modification of the model’s weights or architecture), state (modification of the m...

评测与基准可解释性代码OpenReview 项目页

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