Papers by Jiahuan Pei
StoryMI: Steerable Multi-Agent Therapeutic Dialogue Generation (2026.findings-acl)
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| Challenge: | Motivational interviewing (MI) is a directive, client-centered counseling approach for eliciting clients' motivation for behavioral change. |
| Approach: | They propose a multi-LLM agent framework for controllable MI dialogue generation . therapist and client agents generate MI-coded utterances guided by MI codes . |
| Outcome: | The proposed framework can generate fluent dialogues with minimal intervention time and a high level of evaluation. |
Syllogistic Reasoning for Legal Judgment Analysis (2023.emnlp-main)
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Wentao Deng, Jiahuan Pei, Keyi Kong, Zhe Chen, Furu Wei, Yujun Li, Zhaochun Ren, Zhumin Chen, Pengjie Ren
| Challenge: | Legal judgment assistants are developing fast due to impressive progress of large language models. |
| Approach: | They construct and manually correct a syllogistic reasoning dataset for legal judgment analysis using large language models as benchmarks. |
| Outcome: | The proposed dataset contains 11,239 criminal cases covering 4 criminal elements, 80 charges and 124 articles. |
Intent-calibrated Self-training for Answer Selection in Open-domain Dialogues (2023.tacl-1)
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| Challenge: | Existing answer selection models require large amounts of labeled data to produce accurate answers. |
| Approach: | They propose intent-calibrated self-training to calibrate answer labels using labeled data . they propose intentcalibration to improve quality of pseudo answer labels . |
| Outcome: | The proposed intent-calibrated answer selection paradigm outperforms baselines with 1%, 5%, and 10% labeled data on two benchmark datasets. |
MELoRA: Mini-Ensemble Low-Rank Adapters for Parameter-Efficient Fine-Tuning (2024.acl-long)
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Pengjie Ren, Chengshun Shi, Shiguang Wu, Mengqi Zhang, Zhaochun Ren, Maarten Rijke, Zhumin Chen, Jiahuan Pei
| Challenge: | Large language models (LLMs) are the default paradigm for natural language processing (NLP) as the models’ scale and the diversity of tasks increase, fine-tuning becomes infeasible. |
| Approach: | They propose to freeze original pretrained weights and train a group of mini LoRAs with only a small number of parameters and reduce their rank by 8 times . |
| Outcome: | The proposed model uses fewer trainable parameters while maintaining a higher rank, thereby offering improved performance potential. |
Rethinking the Alignment of Psychotherapy Dialogue Generation with Motivational Interviewing Strategies (2025.coling-main)
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| Challenge: | Motivational interviewing (MI) is a client-centered counseling technique that encourages individuals to change behaviors through emphatic conversations. |
| Approach: | They propose to use large language models to generate more controllable dialogues with explainability by prompting LLMs to predict appropriate strategies as reasoning and utilizing these strategies to guide dialogue generation. |
| Outcome: | The proposed model generates more controllable and explainable dialogues with a set of MI skills. |
SciText2Eq: Assessing LLMs for Explainable Equation Generation for Scientific Creativity (2026.findings-acl)
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| Challenge: | Prior work has addressed problems in unstructured grounding, multi-equation dependency, and human-aligned evaluation. |
| Approach: | They construct a dataset of scientific texts and evaluate it using an explainable equation generation workflow using automatic metrics and human judgments. |
| Outcome: | The proposed model achieves moderate performance on lexical and syntactic similarity, but struggles with semantic accuracy. |
Eliciting Motivational Interviewing Skill Codes in Psychotherapy with LLMs: A Bilingual Dataset and Analytical Study (2024.lrec-main)
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Xin Sun, Jiahuan Pei, Jan de Wit, Mohammad Aliannejadi, Emiel Krahmer, Jos T.P. Dobber, Jos A. Bosch
| Challenge: | Motivational interviewing (MI) is an essential, directive, client-centered counseling technique. |
| Approach: | They propose a bilingual dataset of MI conversations in English and Dutch . they propose an approach to elicit MISC expertise from Large language models . |
| Outcome: | The proposed approach yields results aligned with expert annotations and maintains consistent performance across languages. |
Conversational Education at Scale: A Multi-LLM Agent Workflow for Procedural Learning and Pedagogic Quality Assessment (2025.findings-emnlp)
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| Challenge: | Existing work on large language models lacks scalability and assesses pedagogic quality. |
| Approach: | They propose a multi-agent workflow leveraging large language models to simulate interactive teaching-learning conversations. |
| Outcome: | The proposed workflow integrates teacher and learner agents, an interaction manager, and an evaluator to facilitate procedural learning and assess pedagogic quality. |
Autonomous Workflow for Multimodal Fine-Grained Training Assistants Towards Mixed Reality (2024.findings-acl)
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Jiahuan Pei, Irene Viola, Haochen Huang, Junxiao Wang, Moonisa Ahsan, Fanghua Ye, Jiang Yiming, Yao Sai, Di Wang, Zhumin Chen, Pengjie Ren, Pablo Cesar
| Challenge: | a fine-grained, comprehensive understanding of multimodal environments remains under-explored. |
| Approach: | They propose an automated workflow for integrating AI agents into extended reality (XR) they propose a cerebral language agent that integrates LLM with memory, planning, and interaction with XR tools and a vision-language agent . |
| Outcome: | The proposed workflow integrates AI agents seamlessly into extended reality (XR) applications for fine-grained training. |
Assessing “Implicit” Retrieval Robustness of Large Language Models (2024.emnlp-main)
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| Challenge: | Retrieval-augmented generation (RAG) is a framework to enhance large language models with external knowledge, but its effectiveness is constrained by the retrieval robustness of the model. |
| Approach: | They propose to use gold and distracting context to fine-tune models to handle relevant or irrelevant retrieved context in an end-to-end manner. |
| Outcome: | The proposed model performs better when gold and distracting context are used, while still extracting correct answers when retrieval is accurate. |