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Lun-Wei Ku

ACL 2026×2AAAI 2026效率与系统评测与基准数据与预训练推理对齐与偏好
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0 一作 · 3 篇 · ACL 2026 / AAAI 2026

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2026 顶会论文

3
作者位次 #4/5ACL 2026Workshop· ACL System Demonstrations

Expert Calibration Lens for Pruning Mixture of Experts

Luis Frentzen Salim · Chia-Chun Wu · Tran Van Nhiem · Lun-Wei Ku · Yung-Hui Li

摘要

Expert pruning is a practical deployment technique for Mixture-of-Experts (MoE) models. It reduces resource usage and mitigates expert redundancy, but its success depends strongly on the calibration set used for pruning. In domain-general settings, it is unclear which properties of the calibratio...

效率与系统评测与基准数据与预训练OpenReview 项目页
作者位次 #6/6ACL 2026Findings· Findings of ACL

Beyond Evidence: Belief-Chain Conditioning for Persuasive Misinformation Debunking Explanation

Yi-Li Hsu · Li-Wun Chang · Chun-Yu Hsu · Wei-Kuan Shih · Aiping Xiong · Lun-Wei Ku

摘要

As AI systems increasingly mediate everyday communication, large language models (LLMs) are expected not only to provide factually accurate responses but also to generate explanations that engage with users’ mental states. We build on the concept of cognitive chains—structured representations of...

推理对齐与偏好可解释性OpenReview 项目页

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