The paper introduces a method to predict whether volunteer mental‑health crisis counselors will improve their conversational skills early in their careers. It focuses on identifying moments counselors initially struggle with, tracking how they adapt to similar moments in later conversations, and using these early adaptations to forecast long‑term improvement. The approach outperforms baseline models that rely solely on conversation transcripts.
By Vivian Nguyen, Lillian Lee, Elizabeth A. Olson, Cristian Danescu-Niculescu-Mizil
The paper introduces FIGS, a dual‑axis evaluation framework for multi‑turn sycophancy that avoids penalizing empathy. It uses a 10‑turn conversational simulator with 500 diverse scenarios to test whether models stay truthful while keeping praise proportional, and whether they show calibrated validation of user feelings. The study finds that current models either drift toward sycophancy or become overly detached, highlighting an unresolved trade‑off in sustained dialogue.
By Sidharth Pulipaka, Ruta Binkyte, Ivaxi Sheth, Sahar Abdelnabi
arXiv:2601. 00181v3 Announce Type: replace-cross Abstract: We address two persistent gaps in Emotion Recognition in Conversation: which modeling choices materially affect performance, and how recognition findings connect to interpretable discourse-level patterns.
By Cheonkam Jeong, Adeline Nyamathi
arXiv:2609.17180v1 Announce Type: new
Abstract: Multimodal counselor response generation (MCRG) aims to generate an appropriate counselor response from multimodal dialogue histories. Progress is limi...
By Wenjie Zheng, Qiming Xie, Jianfei Yu, Rui Xia
arXiv:2609.14118v1 Announce Type: cross
Abstract: Online understanding of who is talking to the camera wearer is a key capability for egocentric social interaction. However, existing talk-to-me (TTM)...
By Feiyu Du, Xi He, Jia Li, Yapeng Tian, Weili Wu
arXiv:2607. 24191v1 Announce Type: cross Abstract: Conversational stance detection has shifted from static text analysis to dynamic multimodal modeling.
By Heyan Chai, Xin Li, Wenjie Wang, Jianyang Qin, Chaoyang Li, Lu Wang, Hao Chen, Qing Liao
The paper investigates the role of minimal responses—short, empathic utterances—in psychological counseling, noting that such brief replies are common in human dialogues but underrepresented in large language model (LLM) outputs. Using a two‑stage filtering approach and contextual verification with an LLM, the authors systematically analyze minimal responses across multiple counseling datasets. They find that while strong commercial LLMs can produce minimal replies when prompted, they often fail to judge when these replies are appropriate, and counseling‑specific models trained on synthetic data tend to generate longer, content‑rich responses instead.
By Zhiyang Qi
arXiv:2609.22778v1 Announce Type: new
Abstract: Multimodal large language models (MLLMs) are increasingly used as evaluators, yet their reliability in professional assessment tasks that require exper...
By Yuhan Lu, Yi Yao, Hua Shen, Katie Aafjes-van Doorn, Zhaonan Wang
arXiv:2607.23648v2 Announce Type: replace
Abstract: Using large language models (LLMs) to assist psychological counseling is an important task in the field of natural language processing. The constru...
By Kaitong Weng, Lixin Liu, Zihao Liu, Bo Wang, Shiguang Ni
arXiv:2609.05806v1 Announce Type: new
Abstract: Emotion Recognition in Conversations (ERC) aims to identify speakers' emotions in multi-turn dialogue. Accurate emotion recognition can support a wide...
By Amir Ben Khalifa, Fanny Bezancon, Amine Trabelsi, Bessam Abdulrazak
The study examines how emotions are represented across layers of large language models (LLMs) by probing eight 1B–9B open‑weight models on three datasets (Twitter, Reddit, autobiographical narratives). It finds that the optimal probing layer varies systematically with the dataset, moving from near‑input layers to deeper layers, and that targeted forward‑pass interventions on these layers degrade performance more than random interventions. Additionally, the selected layers transfer across datasets and emotion categories, and early‑exit representations from these layers outperform full‑depth exits by an average of 6.9 percentage points.
By Tian Fang, Ga\"el Guibon, Davide Buscaldi
HUG‑VIS is a unified multimodal benchmark for human‑centered visual intelligence, comprising 8,400 half‑body videos of 30 professional actors performing 280 emotion‑action prompts in Mandarin. The dataset provides synchronized video, audio, text, and alpha mattes for four tasks—human emotion recognition, video generation, voice cloning, and video matting—allowing evaluation of both open‑ and closed‑source models under a zero‑shot protocol. Results reveal that linguistic cues dominate emotion recognition, visual affect is weakest, and that automatic metrics and human judgments diverge in generation and cloning tasks, while motion‑related boundary fidelity remains a key challenge for matting.
By Fei Ma, Zebang Cheng, Minghui Li, Hongbo Xu, Yuyong Tan, Yihua Shao, Hanling Wang, Zhou Liu, Yuqing Gao, Dong Wang, Long Ma, Laizhong Cui, Nicu Sebe, Qi Tian