The mismatch between the growing demand for psychological counseling and the limited availability of services has motivated research into the application of Large Language Models (LLMs) in this domain. Consequently, there is a need for a robust and unified benchmark to assess the counseling competence of various LLMs. Existing works, however, are limited by unprofessional client simulation, static question-and-answer evaluation formats, and unidimensional metrics. These limitations hinder their effectiveness in assessing a model's comprehensive ability to handle diverse and complex clients. To address this gap, we introduce CARE-Bench, a dynamic and interactive automated benchmark. It is built upon diverse client profiles derived from real-world counseling cases and simulated according to expert guidelines. CARE-Bench provides a multidimensional performance evaluation grounded in established psychological scales. Using CARE-Bench, we evaluate several general-purpose LLMs and specialized counseling models, revealing their current limitations. In collaboration with psychologists, we conduct a detailed analysis of the reasons for LLMs' failures when interacting with clients of different types, which provides directions for developing more comprehensive, universal, and effective counseling models.

In recent years, Large Language Models (LLMs) have made significant progress in emotional support dialogue.However, there are two major challenges for LLM-based support systems.First, users may be hesitant to fully disclose their emotions at the outset.Second, direct probing or excessive questioning can induce discomfort or even resistance.To bridge this gap, we propose COCOON, a proactive emotional support framework that leverages principles of active listening to uncover implicit user needs.We design a multi-stage data curation pipeline and an annotation mechanism for support strategies.Based on this framework, we build COCOON-Llama3, a fine-tuned large language model, and evaluate it using both standard metrics and psychological scales.Experimental results indicate that our model more effectively elicits implicit emotional needs and delivers empathetic support compared to existing baselines, suggesting its utility for building more inclusive emotional support dialogue systems.

Detecting depression through users’ social media posting history is crucial for enabling timely intervention; however, irrelevant content within these posts negatively impacts detection performance. Thus, it is crucial to extract pertinent content from users’ complex posting history. Current methods utilize frozen screening models, which can miss critical information and limit overall performance due to isolated screening and detection processes. To address these limitations, we propose E2-LPS End-to-End Learnable Psychiatric Scale Guided Risky Post Screening Model) for jointly training our screening model, guided by psychiatric scales, alongside the detection model. We employ a straight-through estimator to enable a learnable end-to-end screening process and avoid the non-differentiability of the screening process. Experimental results show that E2-LPS outperforms several strong baseline methods, and qualitative analysis confirms that it better captures users’ mental states than others.

The integration of multimodal information, particularly visual content, into dialogue systems has primarily focused on interpreting user-provided inputs, while comparatively little attention has been given to the proactive use of such content to enhance responses. In this paper, we explore a new research direction that addresses this gap by enabling dialogue systems to autonomously determine when and how to supplement textual responses with relevant images, based on conversational context and user intent. To support this goal, we propose AMI (Automated Multimodal Insertion), a novel framework for dynamic, context-aware multimodal supplementation in dialogue. We also introduce RID (Response with Appropriate Image Dataset), a bilingual (Chinese-English) multimodal multi-turn dialogue dataset designed to train and evaluate systems on this capability. RID features fine-grained annotations on image insertion timing and rationale, along with carefully aligned image-text pairs to ensure semantic coherence. Our experiments demonstrate that models trained with RID not only generate more informative and engaging responses, but also exhibit a stronger ability to leverage visual content when it is truly beneficial. These findings highlight the potential of proactive multimodal supplementation and offer new insights for advancing the development of intelligent, human-like dialogue systems. Code and data are available at: https://github.com/Tanthen/SCIR-AMI.

Aspect-level multimodal sentiment analysis, which aims to identify the sentiment of the target aspect from multimodal data, recently has attracted extensive attention in the community of multimedia and natural language processing.Despite the recent success in textual aspect-based sentiment analysis, existing models mainly focused on utilizing the object-level semantic information in the image but ignore explicitly using the visual emotional cues, especially the facial emotions.How to distill visual emotional cues and align them with the textual content remains a key challenge to solve the problem.In this work, we introduce a facesensitive image-to-emotional-text translation (FITE) method, which focuses on capturing visual sentiment cues through facial expressions and selectively matching and fusing with the target aspect in textual modality.To the best of our knowledge, we are the first that explicitly utilize the emotional information from images in the multimodal aspect-based sentiment analysis task.Experiment results show that our method achieves state-of-the-art results on the Twitter-2015 and Twitter-2017 datasets.The improvement demonstrates the superiority of our model in capturing aspect-level sentiment in multimodal data with facial expressions 1 .