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Qinyuan Wu

ACL 2026×2ICLR 2026×2理论与学习动力学评测与基准智能体与工具对齐与偏好社会与人本
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合作网络12
1 一作 · 4 篇 · ACL 2026 / ICLR 2026

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2026 顶会论文

4
作者位次 #4/8ACL 2026Poster· Natural Language Processing

Fine-tuning vs. In-context Learning in Large Language Models: A Formal Language Learning Perspective

Bishwamittra Ghosh · Soumi Das · Till Speicher · Qinyuan Wu · Mohammad Aflah Khan · Deepak Garg · Krishna P. Gummadi · Evimaria Terzi

摘要

Large language models (LLMs) operate in two fundamental learning modes – fine-tuning (FT) and in-context learning (ICL) – raising key questions about which mode yields greater language proficiency and whether they differ in their inductive biases. Prior studies comparing FT and ICL have yielded m...

评测与基准理论与学习动力学强化学习与后训练OpenReview 项目页
作者位次 #3/6ACL 2026Findings· Findings of ACL

Characterizing Web Search in The Age of Generative AI

Elisabeth Kirsten · Jost Große Perdekamp · Qinyuan Wu · Mihir Upadhyay · Krishna P. Gummadi · Muhammad Bilal Zafar

摘要

The advent of LLMs has given rise to generative search, a new search paradigm in which LLMs retrieve information from the web related to a query and synthesize it into a single, coherent response. This paradigm differs fundamentally from traditional web search, where results are returned as a ran...

检索与知识评测与基准OpenReview 项目页

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