Clarification need prediction (CNP) is a key task in conversational search, aiming to predict whether to ask a clarifying question or give an answer to the current user query. However, current research on CNP suffers from the issues of limited CNP training data and low efficiency. In this paper, we propose a zero-shot and efficient CNP framework (Zef-CNP), in which we first prompt large language models (LLMs) in a zero-shot manner to generate two sets of synthetic queries: ambiguous and specific (unambiguous) queries. We then use the generated queries to train efficient CNP models. Zef-CNP eliminates the need for human-annotated clarification-need labels during training and avoids the use of LLMs with high query latency at query time. To further improve the generation quality of synthetic queries, we devise a topic-, information-need-, and query-aware chain-of-thought (CoT) prompting strategy (TIQ-CoT). Moreover, we enhance TIQ-CoT with counterfactual query generation (CoQu), which guides LLMs first to generate a specific/ambiguous query and then sequentially generate its corresponding ambiguous/specific query. Experimental results show that Zef-CNP achieves superior CNP effectiveness and efficiency compared with zero- and few-shot LLM-based CNP predictors.

In psychological practice, standardized questionnaires serve as essential tools for assessing mental constructs (e.g., attitudes, traits, and emotions) through structured questions (aka items). With the increasing prevalence of social media platforms where users share personal experiences and emotions, researchers are exploring computational methods to leverage this data for rapid mental health screening. In this study, we propose a novel adaptive Retrieval-Augmented Generation (RAG) approach that completes psychological questionnaires by analyzing social media posts. Our method retrieves the most relevant user posts for each question in a psychological survey and uses Large Language Models (LLMs) to predict questionnaire scores in a zero-shot setting. Our findings are twofold. First we demonstrate that this approach can effectively predict users' responses to psychological questionnaires, such as the Beck Depression Inventory II (BDI-II), achieving performance comparable to or surpassing state-of-the-art models on Reddit-based benchmark datasets without relying on training data. Second, we show how this methodology can be generalized as a scalable screening tool, as the final assessment is systematically derived by completing standardized questionnaires and tracking how individual item responses contribute to the diagnosis, aligning with established psychometric practices.

The increasing demand for mental health services has highlighted the need for innovative solutions, particularly in the realm of psychological conversational AI, where the availability of sensitive data is scarce. In this work, we explored the development of a system tailored for mental health support with a novel approach to psychological assessment based on explainable emotional profiles in combination with empathetic conversational models, offering a promising tool for augmenting traditional care, particularly where immediate expertise is unavailable. Our work can be divided into two main parts, intrinsecaly connected to each other. First, we present RACLETTE, a conversational system that demonstrates superior emotional accuracy compared to state-of-the-art benchmarks in both understanding users' emotional states and generating empathetic responses during conversations, while progressively building an emotional profile of the user through their interactions. Second, we show how the emotional profiles of a user can be used as interpretable markers for mental health assessment. These profiles can be compared with characteristic emotional patterns associated with different mental disorders, providing a novel approach to preliminary screening and support.