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Hunter Lang

ACL 2026×2可解释性多模态长上下文与架构强化学习与后训练评测与基准
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0 一作 · 2 篇 · ACL 2026

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2026 顶会论文

2
作者位次 #9/13ACL 2026Poster· Natural Language Processing

When One LLM Drools, Multi- LLM Collaboration Rules

Shangbin Feng · Wenxuan Ding · Alisa Liu · Zifeng Wang · Weijia Shi · Yike Wang · Shannon Zejiang Shen · Xiaochuang Han · Hunter Lang · Chen-Yu Lee · Tomas Pfister · Yejin Choi · Yulia Tsvetkov

摘要

This position paper argues that in many realistic (i.e., complex, contextualized, subjective) scenarios, one LLM is not enough to produce a reliable output. We challenge the status quo of relying solely on a single general-purpose LLM and argue for multi-LLM collaboration to better represent the...

可解释性多模态长上下文与架构OpenReview 项目页
作者位次 #18/25ACL 2026Poster· Natural Language Processing

A dvanced IF : Rubric-Based Benchmarking and Reinforcement Learning for Advancing LLM Instruction Following

Yun He · Wenzhe Li · Hejia Zhang · Songlin Li · Karishma Mandyam · Sopan Khosla · Yuanhao Xiong · Nanshu Wang · Xiaoliang Peng · Beibin Li · Shengjie Bi · Shishir G. Patil · Qi Qi · Shengyu Feng · Julian Katz-Samuels · Richard Yuanzhe Pang · Sujan Kumar Gonugondla · Hunter Lang · Yue Yu · Yundi Qian · Maryam Fazel-Zarandi · Licheng Yu · Amine Benhalloum · Hany Hassan Awadalla · Manaal Faruqui

摘要

Recent progress in large language models (LLMs) has led to impressive performance on a range of tasks, yet advanced instruction following (IF)—especially for complex, multi-turn, and system-prompted instructions—remains a significant challenge. Rigorous evaluation and effective training for such...

强化学习与后训练评测与基准可解释性OpenReview 项目页

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