Papers by Guanqun Bi

8 papers
A Group Fairness Lens for Large Language Models (2025.findings-emnlp)

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Challenge: Existing methods focusing on a few groups lack a comprehensive categorical perspective to evaluate LLMs’ potential biases and unfairness.
Approach: They propose to evaluate LLM biases from a group fairness lens using a hierarchical schema characterizing diverse social groups.
Outcome: The proposed method mitigates biases in LLMs from a group fairness lens and encapsulates target-attribute combinations across multiple dimensions.
Psychology-guided Controllable Story Generation (2022.coling-1)

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Challenge: Existing controllable story generation systems ignore the psychological changes of the protagonists and focus on the appointed keywords or emotions.
Approach: They propose a Psychology-guided Controllable Story Generation System (PICS) that generates stories that adhere to the given leading context and desired psychological state chains for the protagonist.
Outcome: The proposed system outperforms baselines and shows that it can generate stories with more consistent psychological changes.
MAGI: Multi-Agent Guided Interview for Psychiatric Assessment (2025.findings-acl)

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Challenge: Existing large language models (LLMs) do not align with psychiatric diagnostic protocols.
Approach: They propose a framework that transforms the Mini International Neuropsychiatric Interview into automatic computational workflows through coordinated multi-agent collaboration.
Outcome: The proposed framework transforms the gold-standard Mini International Neuropsychiatric Interview (MINI) into automatic computational workflows through coordinated multi-agent collaboration.
COMMA: Modeling Relationship among Motivations, Emotions and Actions in Language-based Human Activities (2022.coling-1)

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Challenge: Existing methods for modeling motivations, emotions and actions in language-based human activities have been limited.
Approach: They propose to model motivations, emotions and actions in language-based human activities using a dataset called Story Commonsense.
Outcome: The proposed model can better reveal the essential relationship between motivations, emotions and actions than existing methods.
S^4: Operationalizing Speech Act Theory for Strategic Semi-Structured Psychiatric Interview (2026.acl-long)

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Challenge: Existing methods for psychiatric interviewing degenerate into rigid interrogation or aimless chitchat due to a lack of strategic planning.
Approach: They propose a framework for psychiatric interviewing grounded in Speech Act Theory that integrates a large-scale dataset with fine-grained psychic speech act annotations.
Outcome: The proposed framework outperforms baselines in psychiatric interviewing.
DiffusEmp: A Diffusion Model-Based Framework with Multi-Grained Control for Empathetic Response Generation (2023.acl-long)

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Challenge: Existing methods to generate empathetic responses are monotonous and generic, resulting in shallow empathy and few connections to the context.
Approach: They propose to use explicit control to guide the empathy expression and a framework DiffusEmp to unify the utilization of dialogue context and attribute-oriented control signals.
Outcome: The proposed framework outperforms baselines on EmpatheticDialogue in terms of controllability, informativeness, diversity, and diversity without the loss of context-relatedness.
CharacterGLM: Customizing Social Characters with Large Language Models (2024.emnlp-industry)

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Challenge: Character-based dialogue systems (CharacterDial) allow users to customize social characters for social interactions.
Approach: They will collect a large-scale Chinese corpus of characters with diverse categories and behaviors and develop CharacterGLM models to address these challenges.
Outcome: Experiments show that CharacterGLM outperforms most popular open- and closed-source LLMs and performs comparable to GPT-4.
PsychePass: Calibrating LLM Therapeutic Competence via Trajectory-Anchored Tournaments (2026.findings-acl)

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Challenge: evaluating therapeutic competence of large language models remains challenging due to unstructured and longitudinal nature of counseling.
Approach: They propose a framework that calibrates the therapeutic competence of LLMs via trajectory-anchored tournaments.
Outcome: The proposed framework calibrates the therapeutic competence of LLMs via trajectory-anchored tournaments.

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