Papers with user modeling
Enhancing Reliability in Community Question Answering with an Expert-Oriented RAG System (2026.findings-eacl)
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| Challenge: | Existing LLMs require substantial computational resources and are prone to generating hallucinated or unreliable content. |
| Approach: | They propose an expert-oriented Retrieval-Augmented Generation framework which leverages user modeling to identify archived questions with answers that fully or partially address the user’s new query. |
| Outcome: | The proposed framework synthesizes expert-written answers from similar questions to generate unified answers. |
SEAD: Self-Evolving Agent for Multi-Turn Service Dialogue (2026.findings-acl)
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| Challenge: | Large Language Models have demonstrated remarkable capabilities in open-domain dialogues, but their performance in service dialogues remains suboptimal. |
| Approach: | They propose a framework that enables agents to learn effective strategies without large-scale human annotations. |
| Outcome: | The proposed framework decouples user modeling into two components that provide adaptive training scenarios rather than acting as an unfair adversary. |
HieRec: Hierarchical User Interest Modeling for Personalized News Recommendation (2021.acl-long)
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| Challenge: | Existing news recommendation methods learn a single user embedding for each user from their previous behaviors to represent their overall interest. Existing methods only learn 'one' embeddable representation vectors to model user interest. |
| Approach: | They propose a news recommendation method with hierarchical user interest modeling that captures user interest in news rather than a single user embedding. |
| Outcome: | The proposed method can better capture multi-grained user interest in news. |