ACL

iQUEST: An Iterative Question-Guided Framework for Knowledge Base Question Answering

iQUEST: 一种迭代式问题引导的知识库问答框架

Shuai Wang, Yinan Yu

机构 * Department of Computer Science and Engineering（计算机科学与工程系） 纠错

AI总结 iQUEST通过迭代分解复杂查询和集成图神经网络，提升多跳知识库问答的推理准确性与连贯性。

Comments Accepted to the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), Main Track

Large Language Models as Oracles for Ontology Alignment

大型语言模型作为本体对齐的预言机

Sviatoslav Lushnei, Dmytro Shumskyi, Severyn Shykula, Ernesto Jimenez-Ruiz, Artur d'Avila Garcez

机构 * City St George’s, University of London, UK（伦敦大学城市圣乔治学院） 纠错

AI总结 本文提出利用大型语言模型作为预言机，通过验证高不确定性的对应关系子集，提升本体对齐任务的性能，在OAEI 2025中取得优异成绩。

Comments Paper accepted at the 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2026), main conference. 21 pages

Versatile Framework for Song Generation with Prompt-based Control

具有基于提示控制的多功能歌曲生成框架

Yu Zhang, Wenxiang Guo, Changhao Pan, Zhiyuan Zhu, Ruiqi Li, Jingyu Lu, Rongjie Huang, Ruiyuan Zhang, Zhiqing Hong, Ziyue Jiang, Zhou Zhao

机构 * Zhejiang University（浙江大学） 纠错

AI总结 VersBand通过多任务框架实现基于提示的可控高质量歌曲生成，包含人声、伴奏、歌词和旋律生成模型，实验表明其在多个任务中表现优异。

Comments Accepted by Findings of EMNLP 2025

Journal ref Findings of the Association for Computational Linguistics: EMNLP 2025