Papers by Xueqiang Xu
s3: You Don’t Need That Much Data to Train a Search Agent via RL (2025.emnlp-main)
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| Challenge: | Existing approaches to optimize retrieval using search-only metrics ignore downstream utility and fine-tune entire LLM to jointly reason and retrieve limit retrieval utility and compatibility with frozen or proprietary models. |
| Approach: | They propose a lightweight, model-agnostic framework that decouples the searcher from the generator and trains the search user using a Gain Beyond RAG reward. |
| Outcome: | The proposed framework outperforms baselines trained on over 70 more data with 2.4k training samples. |
LogiCoL: Logically-Informed Contrastive Learning for Set-based Dense Retrieval (2025.emnlp-main)
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| Challenge: | Current dense retrievers struggle with queries with logical connectives, a use case that is often overlooked but important in downstream applications. |
| Approach: | They propose a logically-informed contrastive learning objective for dense retrievers that learns to respect the subset and mutually exclusive set relation between query results. |
| Outcome: | The proposed model improves retrieval performance and consistency on entity retrieval tasks. |
Zero-Shot Open-Schema Entity Structure Discovery (2026.eacl-long)
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Xueqiang Xu, Jinfeng Xiao, James Barry, Mohab Elkaref, Jiaru Zou, Pengcheng Jiang, Yunyi Zhang, Maxwell J Giammona, Geeth De Mel, Jiawei Han
| Challenge: | Existing methods based on large language models (LLMs) rely heavily on predefined entity attribute schemas or annotated datasets, often leading to incomplete extraction results. |
| Approach: | They propose a novel approach to entity structure extraction that does not require any schema or annotated datasets. |
| Outcome: | Experiments show that ZOES improves LLMs’ ability to extract more complete entity structures across three different domains, showcasing both the effectiveness and generalizability of the method. |
Towards Multi-Relational Multi-Hop Reasoning over Dense Temporal Knowledge Graphs (2024.findings-acl)
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| Challenge: | Temporal knowledge graph reasoning is a crucial task for answering time-dependent questions within a knowledge graph (KG). |
| Approach: | They propose a temporal KG reasoning benchmark with over 200k entities and 960k questions that facilitate complex, multi-relational and multi-hop reasoning. |
| Outcome: | The proposed model is able to conduct pattern-aware and time-sensitive reasoning across temporal KGs and is scalable to a wide range of data conditions. |