Papers by Xiangmin Xu
PsyDT: Using LLMs to Construct the Digital Twin of Psychological Counselor with Personalized Counseling Style for Psychological Counseling (2025.acl-long)
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| Challenge: | Existing mental health LLMs do not consider the fact that different psychological counselors exhibit different personal styles. |
| Approach: | They propose a framework that uses LLMs to construct the digital twin of psychological counselor with personalized counseling style. |
| Outcome: | The proposed framework can synthesize multi-turn dialogues that closely resemble real-world counseling cases and demonstrate better performance compared to baselines. |
TailorRPA: A Retrieval-Based Framework for Eliciting Personalized and Coherent Role-Playing Agents in General Domain (2025.findings-emnlp)
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| Challenge: | a recent study has shown that general domain oriented role-playing agents can maintain character properties in a wide range of tasks beyond scenario based chit-chatting. |
| Approach: | They propose a retrieval-based framework to harvest tailored general domain instructions . they use general-domain protective queries to shape character-wise knowledge boundary . |
| Outcome: | The proposed framework improves integration of fine-grained memories and protects character knowledge boundary . it also improves character hallucination in general domain, compared to baseline methods . |
TreeRAG: Unleashing the Power of Hierarchical Storage for Enhanced Knowledge Retrieval in Long Documents (2025.findings-acl)
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| Challenge: | Traditional RAG frameworks struggle to retrieve all relevant knowledge points . a new approach to retrieve long documents is proposed to improve performance in NLP . |
| Approach: | They propose a tree-based approach to document knowledge retrieval that preserves hierarchical structure . treeRAG is a key technique for enhancing the text generation capabilities of Large Language Models . |
| Outcome: | The proposed approach improves recall quality and precision compared to existing methods and better performance to question-answering tasks. |
CATCH: A Novel Data Synthesis Framework for High Therapy Fidelity and Memory-Driven Planning Chain of Thought in AI Counseling (2025.findings-emnlp)
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| Challenge: | Existing studies employ a one-time generation approach to synthesize multi-turn dialogue samples, resulting in low therapy fidelity and failing to capture decision-making rationale behind each response. |
| Approach: | They propose a data synthesis framework that synthesizes multi-turn dialogue samples and incrementally generates stage-aligned counseling dialogues. |
| Outcome: | The proposed framework significantly improves therapy fidelity and logical coherence in AI counseling. |
Momoka-RAG: MCTS-Organized Mapping of Knowledge Associations for Long-Document Retrieval Augmented Generation (2026.findings-acl)
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| Challenge: | Existing frameworks that rely on fixed-length chunking are unsuitable for long-document tasks due to their passive and mechanical approach to knowledge structure. |
| Approach: | They propose a framework that utilizes Monte Carlo Tree Search to proactively uncover connections among chunks and construct optimal semantic information paths with the objective of completing semantic relationships. |
| Outcome: | The proposed framework achieves higher precision while maintaining competitive recall compared to other RAG frameworks. |
QuantAgents: Towards Multi-agent Financial System via Simulated Trading (2025.findings-emnlp)
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| Challenge: | Existing LLM-based agent models exhibit significant deviations from real-world fund companies. |
| Approach: | They propose a multi-agent financial system that incorporates simulated trading . they propose simulated trades are evaluated without assuming actual risks . |
| Outcome: | The proposed system evaluates various investment strategies without assuming actual risks without involving real-world investors. |
SAKI-RAG: Mitigating Context Fragmentation in Long-Document RAG via Sentence-level Attention Knowledge Integration (2025.emnlp-main)
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| Challenge: | Traditional Retrieval-Augmented Generation (RAG) frameworks segment documents into larger chunks to preserve contextual coherence . however, such chunking methods lead to fragmented contexts, isolated chunk semantics, and broken inter-chunk relationships . |
| Approach: | They propose a framework that maintains granular chunks while recovering their intrinsic semantic connections. |
| Outcome: | The proposed framework achieves better recall and precision compared to other RAG frameworks in long-document retrieval scenarios. |
SoulChat: Improving LLMs’ Empathy, Listening, and Comfort Abilities through Fine-tuning with Multi-turn Empathy Conversations (2023.findings-emnlp)
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| Challenge: | Large language models (LLMs) are used in psychological counseling to provide universal advice. |
| Approach: | They constructed a multi-turn empathetic conversation dataset with 2 million samples . they found that the model's empathy ability is enhanced when finetuning . |
| Outcome: | Experiments show that large language models can be finetuned to provide empathy . but, when applied to mental health or emotional support conversation, there are three main issues . |