Papers by Quan Tu
“In-Dialogues We Learn”: Towards Personalized Dialogue Without Pre-defined Profiles through In-Dialogue Learning (2024.emnlp-main)
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| Challenge: | Existing approaches to personalized dialogue generate pre-defined profiles that are time-consuming and labor-intensive to create. |
| Approach: | They propose a framework that leverages dialogue history to characterize personas without pre-defined profiles. |
| Outcome: | The proposed framework improves BLEU and ROUGE scores on three datasets and human evaluations further validate the proposed method. |
MISC: A Mixed Strategy-Aware Model integrating COMET for Emotional Support Conversation (2022.acl-long)
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| Challenge: | Existing methods for emotional support conversation are too coarse-grained to capture user’s instant mental state and focus on expressing empathy in the response rather than gradually reducing user’ s distress. |
| Approach: | They propose a model which firstly infers the user’s fine-grained emotional status and then responds skillfully using a mixture of strategy. |
| Outcome: | The proposed model infers the user’s fine-grained emotional status and responds skillfully using mixed-up strategy modeling. |
CharacterEval: A Chinese Benchmark for Role-Playing Conversational Agent Evaluation (2024.acl-long)
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| Challenge: | CharacterEval is a benchmark for comprehensive RPCA assessment in Chinese . authors show that Chinese LLMs exhibit more promising capabilities than GPT-4 in role-playing conversation. |
| Approach: | They propose a Chinese benchmark for comprehensive RPCA assessment . they use a dataset of Chinese role-playing dialogues and character profiles . |
| Outcome: | The proposed benchmark demonstrates that Chinese LLMs exhibit more promising capabilities than GPT-4 in Chinese role-playing conversation. |
MARK: Multi-agent Collaboration with Ranking Guidance for Text-attributed Graph Clustering (2025.findings-acl)
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Yiwei Fu, Yuxing Zhang, Chunchun Chen, JianwenMa JianwenMa, Quan Yuan, Rong-Cheng Tu, Xinli Huang, Wei Ye, Xiao Luo, Minghua Deng
| Challenge: | Existing approaches to cluster graphs with GNNs are limited due to label scarcity. |
| Approach: | They propose to leverage large language models to enhance text-attributed graph clustering by using three LLMs as ranking-based supervision signals. |
| Outcome: | The proposed approach generates reliable guidance using collaboration of three LLM-based agents as ranking-based supervision signals. |
InCharacter: Evaluating Personality Fidelity in Role-Playing Agents through Psychological Interviews (2024.acl-long)
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Xintao Wang, Yunze Xiao, Jen-tse Huang, Siyu Yuan, Rui Xu, Haoran Guo, Quan Tu, Yaying Fei, Ziang Leng, Wei Wang, Jiangjie Chen, Cheng Li, Yanghua Xiao
| Challenge: | Existing methods focus on knowledge and linguistic patterns of characters. |
| Approach: | They propose to evaluate character fidelity of role-playing agents with psychological scales . they propose to use psychological scale to measure personality traits of RPAs based on personality traits. |
| Outcome: | The proposed model reproduces character fidelity with psychological scales and shows that it is effective in measuring personality traits. |
Multi-Grained Conversational Graph Network for Retrieval-based Dialogue Systems (2024.lrec-main)
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| Challenge: | Existing methods for retrieval-based dialogues concatenate all turns in the dialogue history as input, ignoring dialogue dependency and structural information between the utterances. |
| Approach: | They propose a multi-grained conversational graph network that considers multiple levels of abstraction from dialogue histories and semantic dependencies within multi-turn dialogues for addressing. |
| Outcome: | The proposed method improves on two benchmarks on open domain dialogues. |
CycleAlign: Iterative Distillation from Black-box LLM to White-box Models for Better Human Alignment (2024.findings-acl)
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| Challenge: | Existing language models that generate harmful responses are constrained by their inherent capability. |
| Approach: | They propose to align large language models with human preferences from AI feedback. |
| Outcome: | The proposed framework improves the alignment of large language models with human preferences from AI feedback. |
An Analysis and Mitigation of the Reversal Curse (2024.emnlp-main)
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| Challenge: | Recent research observes a phenomenon in large language models called the "reversal curse" when dealing with two entities, LLMs excel in handling sequences in the form of "aRb" but when asked "who is Mary Lee Pfeiffer's son?" the LLM exhibits considerable confusion and fails to provide a as the answer . |
| Approach: | They conduct the first-ever study of how the reversal curse happens in large language models . they find that LLMs excel in handling sequences in the form of "aRb" but struggle to provide a satisfactory answer when asked "who is Mary Lee Pfeiffer's son?" |
| Outcome: | The proposed study shows that the reversal curse can stem from specific training objectives . the study also shows that a reverse query can be difficult to understand . |
SSP: Self-Supervised Post-training for Conversational Search (2023.findings-acl)
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| Challenge: | Existing methods to search for information are limited by data scarcity and lack contextual semantic understanding. |
| Approach: | They propose a new post-training paradigm with three self-supervised tasks to initialize the conversational search model to enhance dialogue structure and contextual semantic understanding. |
| Outcome: | The proposed method can initialize the conversational search model to enhance dialogue structure and contextual semantic understanding. |
360∘REA: Towards A Reusable Experience Accumulation with 360∘ Assessment for Multi-Agent System (2024.findings-acl)
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| Challenge: | Recent studies focus on optimizing the agent team or employing self-reflection to iteratively solve complex tasks. |
| Approach: | They propose a hierarchical multi-agent framework that uses 360 assessment to accumulate experience through fine-grained assessment. |
| Outcome: | The proposed framework is based on corporate organizational practices and employs a dual-level experience pool for agents to accumulate experience through fine-grained assessment. |