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Xiangjue Dong

ACL 2026×3检索与知识推理强化学习与后训练效率与系统对齐与偏好
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1 一作 · 3 篇 · ACL 2026

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2026 顶会论文

3
作者位次 #2/7ACL 2026Poster· Natural Language Processing

DMR etriever: A Family of Models for Improved Text Retrieval in Disaster Management

Kai Yin · Xiangjue Dong · Chengkai Liu · Allen Lin · Lingfeng Shi · Ali Mostafavi · James Caverlee

摘要

Effective and efficient access to relevant information is essential for disaster management. However, no retrieval model is specialized for disaster management, and existing general-domain models fail to handle the varied search intents inherent to disaster management scenarios, resulting in inco...

检索与知识强化学习与后训练效率与系统OpenReview 项目页
一作ACL 2026Poster· Natural Language Processing

CHOIR : Harmonizing Structured Persona Diversity for Robust Collaborative LLM Reasoning

Xiangjue Dong · Cong Wang · Maria Teleki · Millennium Bismay · Ruihong Huang · James Caverlee

摘要

Persona-assigned Large Language Models can adopt diverse roles, enabling personalized and context-aware reasoning. However, even minor demographic perturbations in personas, such as simple pronoun swaps, can alter reasoning trajectories, leading to divergent sets of correct answers on reasoning b...

推理对齐与偏好安全OpenReview 项目页
作者位次 #3/10ACL 2026Findings· Findings of ACL

D isast QA : A Comprehensive Benchmark for Evaluating Question Answering in Disaster Management

Zhitong Chen · Kai Yin · Xiangjue Dong · Chengkai Liu · Xiangpeng Li · Bo Li · Junwei Ma · Yiming Xiao · Ali Mostafavi · James Caverlee

摘要

Accurate question answering (QA) in disaster management requires reasoning over uncertain and conflicting information, a setting poorly captured by existing benchmarks built on clean evidence. We introduce DisastQA, a rigorously verified benchmark of 3,000 expert-annotated questions (2,000 multip...

评测与基准检索与知识推理OpenReview 项目页

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