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Yue Yu

ACL 2026×2评测与基准强化学习与后训练可解释性检索与知识效率与系统
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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 #19/25ACL 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
作者位次 #3/6ACL 2026Findings· Findings of ACL

RAG in the Wild: On the (In)effectiveness of LLM s with Mixture-of-Knowledge Retrieval Augmentation

Ran Xu · Yuchen Zhuang · Yue Yu · Haoyu Wang · Wenqi Shi · Carl Yang

摘要

Retrieval-augmented generation (RAG) enhances large language models (LLMs) by integrating external knowledge retrieved at inference time. While RAG demonstrates strong performance on benchmarks largely derived from general-domain corpora like Wikipedia, its effectiveness under realistic, diverse...

检索与知识评测与基准效率与系统OpenReview 项目页

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