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Tian Wang

ACL 2026×2ICLR 2026×2强化学习与后训练社会与人本推理评测与基准效率与系统
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0 一作 · 4 篇 · ACL 2026 / ICLR 2026

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2026 顶会论文

4
作者位次 #9/16ACL 2026Poster· Natural Language Processing

OP e RA : A Dataset of Observation, Persona, Rationale, and Action for Evaluating LLM s on Human Online Shopping Behavior Simulation

Ziyi Wang · Yuxuan Lu · Wenbo Li · Amirali Amini · Bo Sun · Yakov Bart · Weimin Lyu · Jiri Gesi · Tian Wang · Jing Huang · Yu Su · Upol Ehsan · Malihe Alikhani · Toby Jia-Jun Li · Lydia B. Chilton · Dakuo Wang

摘要

Can Large Language models (LLMs) accurately simulate the next web action of a specific user? While LLMs have shown promising capabilities in generating believable human behaviors, evaluating their ability to mimic real user behaviors remains an open challenge, largely due to the lack of high-qual...

评测与基准社会与人本推理OpenReview 项目页
作者位次 #5/13ACL 2026Findings· Findings of ACL

On-Policy Self-Distillation for Efficient Diffusion Language Models with Early-Stage Calibration

Huaisheng Zhu · MingYu Liu · Junze Liu · Zhen Ge · Tian Wang · Jiri Gesi · Dakuo Wang · Weiqi Zhang · Houyu Zhang · Yufan Guo · Xian Li · Bing Yin · sujay sanghavi

摘要

Diffusion Large Language Models (DLLMs) have recently achieved strong performance, e.g., masked diffusion models (MDMs) can surpass autoregressive models (ARMs) in various tasks. However, DLLMs often struggle with inaccurate early-stage predictions due to limited context, which hinders both the m...

推理强化学习与后训练效率与系统OpenReview 项目页

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