Retrieving Responses Useful as References in Counseling via LLM-Generated Structurally Analogous Dialogues

2026-07-20 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, quick

Summary

A new framework addresses the challenge of retrieving "referenceable" past dialogues for assisting response generation in domains like mental health counseling, where finding useful subsequent responses is crucial beyond superficial topic matching. This approach leverages large language models (LLMs) to generate pseudo-similar counseling dialogues, maintaining strict message-level correspondence to original sessions. These generated dialogues serve as weak supervision for contrastive learning, training an embedding model to group histories sharing referenceable counselor responses despite explicit situational differences. Experimental results demonstrate significant performance improvements, with Hit@1 increasing from 0.167 to 0.500 and MRR from 0.232 to 0.576. Human evaluations further confirm enhanced referenceability, raising the average score from 2.22 to 2.66.

Key takeaway

For NLP Engineers developing conversational AI in sensitive domains like mental health, you should consider this framework's approach. By using LLM-generated structurally analogous dialogues as weak supervision for contrastive learning, you can effectively train retrieval models to identify "referenceable" responses. This method significantly improves retrieval performance for nuanced dialogue properties, moving beyond simple topic matching to provide more contextually useful assistance.

Key insights

A framework uses LLM-generated pseudo-dialogues as weak supervision for contrastive learning to retrieve "referenceable" counseling responses.

Principles

• "Referenceability" is a critical property for dialogue retrieval.
• LLMs can generate synthetic data for weak supervision.
• Contrastive learning aligns structurally analogous dialogues.

Method

Large language models generate pseudo-similar counseling dialogues with message-level correspondence. These serve as weak supervision for contrastive learning, training an embedding model to group histories with referenceable subsequent responses.

In practice

• Enhance conversational AI in counseling.
• Improve user support systems.
• Address data scarcity for complex dialogue properties.

Topics

• Counseling AI
• Large Language Models
• Dialogue Retrieval
• Contrastive Learning
• Weak Supervision
• Mental Health Support

Best for: Research Scientist, AI Scientist, NLP Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.