PsyAdvisor: A Plug-and-Play Strategy Advice Planner with Proactive Questioning in Psychological Conversations (2025.acl-long)
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| Challenge: | Current psychological LLMs are constrained by passive response mechanisms, limiting their capacity to deploy proactive strategies for psychological counseling. |
| Approach: | They propose a dataset that provides a multi-turn conversation dataset with interpretive labels including strategy decision logic and reaction attribution. |
| Outcome: | The proposed model significantly improves proactive questioning capacity, conversation depth, and response quality. |
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PsyProbe: Proactive and Interpretable Dialogue through User State Modeling for Exploratory Counseling (2026.findings-eacl)
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| Challenge: | Existing approaches to mental health dialogue are reactive and lack systematic user state modeling for proactive therapeutic exploration. |
| Approach: | They propose a dialogue system designed for the exploration phase of counseling that systematically tracks user psychological states through the PPPPPI framework augmented with cognitive error detection. |
| Outcome: | The proposed system outperforms baseline and ablation modes in automatic evaluation and expert evaluation by a certified counselor. |
Prompting and Evaluating Large Language Models for Proactive Dialogues: Clarification, Target-guided, and Non-collaboration (2023.findings-emnlp)
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| Challenge: | Recent studies have shown that ChatGPT has limitations such as failing to ask clarifying questions to ambiguous queries or refusing problematic user requests. |
| Approach: | They propose a Proactive Chain-of-Thought prompting scheme which augments LLMs with the goal planning capability over descriptive reasoning chains to trigger proactivity. |
| Outcome: | The proposed scheme augments LLMs with the goal planning capability over descriptive reasoning chains to trigger the proactivity of LLM-based proactive dialogue systems. |
PsyDial: A Large-scale Long-term Conversational Dataset for Mental Health Support (2025.acl-long)
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| Challenge: | Existing models for mental health counseling use a privacy-preserving data reconstruction method to reconstruct client-counselor dialogues without removing personally identifiable information due to privacy concerns. |
| Approach: | They propose a privacy-preserving data reconstruction method that reconstructs real-world client-counselor dialogues while mitigating privacy concerns. |
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Proactive Human-Machine Conversation with Explicit Conversation Goal (P19-1)
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| Challenge: | Typical human-machine conversation systems only use utterances and responses as training data, which results in uninformative and inappropriate responses. |
| Approach: | They propose a dataset where one acts as a conversation leader and the other as 'follower' they establish baseline results on a 270K utterances and 30k dialogues dataset using state-of-the-art models. |
| Outcome: | The proposed model can generate diverse multi-turn conversations using knowledge from a new dataset . |
ProactiveEval: A Unified Evaluation Framework for Proactive Dialogue Agents (2026.acl-long)
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| Challenge: | Existing studies on proactive dialogue models focus on domain-specific or task-oriented scenarios, which leads to fragmented evaluations and limits the comprehensive exploration of models’ proactive dialogue abilities. |
| Approach: | They propose a framework for evaluating proactive dialogue capabilities of large language models that decomposes proactive dialogue into target planning and dialogue guidance, establishing evaluation metrics across various domains. |
| Outcome: | The proposed framework decomposes proactive dialogue into target planning and dialogue guidance, establishing evaluation metrics across various domains, and enables automatic generation of diverse and challenging evaluation data. |
Thoughts to Target: Enhance Planning for Target-driven Conversation (2024.emnlp-main)
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| Challenge: | Empirical results demonstrate that our method significantly improves the planning ability of LLMs, especially in target-driven conversations. |
| Approach: | They propose a two-stage framework to improve the LLMs’ capability in planning conversations towards designated targets by distilling natural language plans from a target-driven conversation corpus and generating new plans with demonstration-guided in-context learning. |
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PCQPR: Proactive Conversational Question Planning with Reflection (2024.emnlp-main)
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| Challenge: | Current CQG methods focus on immediate context without strategic consideration of the specified conversational outcome. |
| Approach: | They propose a method that uses a planning algorithm inspired by Monte Carlo Tree Search to generate contextually relevant questions. |
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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. |
Reasoning While Asking: Transforming Reasoning Large Language Models from Passive Solvers to Proactive Inquirers (2026.acl-long)
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| Challenge: | Existing reasoning-oriented LLMs lack a blind self-thinking paradigm . current models fail to recognize when their reasoning is underinformed or based on ambiguous user instructions . |
| Approach: | They propose a new reasoning paradigm that transforms LLMs into proactive inquirers that interleave reasoning with clarification. |
| Outcome: | The proposed model outperforms baseline models on mathematical reasoning, code generation, and document editing. |
Ask-before-Plan: Proactive Language Agents for Real-World Planning (2024.findings-emnlp)
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| Challenge: | despite the advancements of large language models, the potential of LLM-powered agents to comprehend ambiguous user instructions is still under exploration. |
| Approach: | They propose a task that requires agents to predict clarification needs based on conversation and agentenvironment interaction and generate a plan to fulfill the user's demands. |
| Outcome: | The proposed framework is based on a new ask-before-plan benchmark dataset. |