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Qinghua Zhao

ACL 2026×2效率与系统理论与学习动力学对齐与偏好推理可解释性
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一作贡献30
核心/参与12
方向广度10
合作网络9
1 一作 · 2 篇 · ACL 2026

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2026 顶会论文

2
一作ACL 2026Poster· Natural Language Processing

A Layer-wise Analysis of Supervised Fine-Tuning

Qinghua Zhao · Xueling Gong · Xinyu Chen · Zhongfeng Kang · Xinlu Li

摘要

While critical for alignment, Supervised Fine-Tuning (SFT) incurs the risk of catastrophic forgetting, yet the layer-wise emergence of instruction-following capabilities remains elusive. We investigate this mechanism via a comprehensive analysis utilizing information-theoretic, geometric, and opt...

效率与系统理论与学习动力学对齐与偏好OpenReview 项目页
作者位次 #2/5ACL 2026Findings· Findings of ACL

How Chain-of-Thought Works? Tracing Information Flow from Decoding, Projection, and Activation

Hao Yang · Qinghua Zhao · Lei Li · Lingyi Meng · Mengda Yu

摘要

Chain-of-Thought (CoT) prompting significantly enhances model reasoning, yet its internal mechanisms remain poorly understood. We analyze CoT’s operational principles by reversely tracing information flow across decoding, projection, and activation phases. Our quantitative analysis suggests that...

推理可解释性效率与系统OpenReview 项目页

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