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Hua Shen

ACL 2026×2CHI 2026ICML 2026社会与人本对齐与偏好可解释性评测与基准强化学习与后训练
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0 一作 · 4 篇 · ACL 2026 / CHI 2026 / ICML 2026

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2026 顶会论文

4
作者位次 #4/4ICML 2026Poster

Position: Bridge Human Interpretation and Machine Representation With Explicit Specification For Qualitative Data Analysis In LLM Era

Xinyu Pi · Qisen Yang · Chuong Nguyen · Hua Shen

可解释性社会与人本OpenReview
作者位次 #2/2ACL 2026Findings· Findings of ACL

V alue A lignment T ax : Measuring Value Trade-offs in LLM Alignment

Jiajun Chen · Hua Shen

摘要

Existing work on value alignment typically characterizes value relations statically, ignoring how alignment interventions—such as prompting, fine-tuning, or preference optimization—reshape the broader value system. In practice, aligning a target value can implicitly shift other values, creating v...

对齐与偏好评测与基准强化学习与后训练OpenReview 项目页
作者位次 #4/15ACL 2026Findings· Findings of ACL

Real or Robotic? Assessing Whether LLM s Accurately Simulate Qualities of Human Responses in Human- LLM Dialogue

Jonathan Ivey · Shivani Kumar · Jiayu Liu · Hua Shen · Sushrita Rakshit · Rohan Raju · Haotian Zhang · Aparna Ananthasubramaniam · Junghwan Kim · Bowen Yi · Dustin Wright · Abraham Israeli · Anders Giovanni Møller · Lechen Zhang · David Jurgens

摘要

Building datasets for dialogue tasks is expensive and time-consuming, requiring recruitment, training, and data collection from study participants. In response, much recent work has sought to use large language models (LLMs) to simulate both human-human and human-LLM interactions, as they have be...

社会与人本对齐与偏好数据与预训练OpenReview 项目页
作者位次 #5/5CHI 2026Oral· Human-Computer Interaction

The Siren Song of LLMs: How Users Perceive and Respond to Dark Patterns in Large Language Models

Yike Shi · Qing Xiao · Qing Hu · Hong Shen · Hua Shen

社会与人本OpenReview

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