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...
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...
The article surveys evaluation methods for large language model (LLM)-based agents in multi-turn conversations, reviewing nearly 250 scholarly sources through a PRISMA-inspired framework. It introduces two taxonomies: one outlining what to evaluate—task completion, response quality, user experience, memory and context retention, planning, and tool integration—and another detailing how to evaluate, covering annotation-based, automated, hybrid, and self-judging approaches. The framework incorporates both traditional metrics like BLEU and ROUGE and advanced techniques suited to dynamic, interactive dialogues.
arXiv:2608.21969v1 Announce Type: new Abstract: Humans have multiple levels of temporal abstractions on daily interaction and thinking, such as concept perception and strategic planning. Inspired by...
arXiv:2607. 14769v1 Announce Type: cross Abstract: Existing text summarization research has focused much on monologic information (e.
The paper introduces the Affective Flow Language Model (AFlow), which treats multi‑turn emotional support conversations as an evolving affective utility flow along dialogue trajectories. AFlow searches diverse support paths, estimates utilities of intermediate states, and employs Affective Flow Preference Optimization (AFPO) to propagate downstream preference signals to earlier states, enabling consistent strategy transitions. Experiments on ExTES and ESConv demonstrate improved strategy alignment, response diversity, and generation quality across various model settings.
The paper introduces CLEAR, a framework for Multimodal Emotion Recognition in Conversation that couples contextual representations with local discriminative cues. It replaces emotion label generation with continuous internal representations from a vision‑language model and trains a classifier on these representations. Sample‑adaptive residual coupling is used to integrate textual and acoustic evidence from the target utterance into the contextual representation, improving emotion recognition performance on IEMOCAP and MELD datasets.
SocialArena is a benchmark designed to evaluate the social intelligence of large language models (LLMs) across three dimensions: role‑playing ability, mental state reasoning, and behavioral reasoning. It introduces a role‑playing dialogue paradigm with explicit character thinking to assess these capabilities in realistic human interactions, and a U‑TRIMF framework to synthesize diverse social contexts. The evaluation relies on a human‑annotated arena‑style protocol, supported by a $132.4K investment in recruiting and training annotators who compare model responses and provide preference labels and critiques.
The paper introduces an LLM-based framework for continuous dimensional emotion evaluation in multimodal dialogue, combining discrete emotion recognition with Valence-Arousal-Dominance (VAD) assessment on the IEMOCAP dataset. It incorporates acoustic cues as natural language descriptions via the SpeechCueLLM approach and evaluates six models from the LLaMA, GPT, and Qwen families using zero-shot, few-shot, and LoRA fine-tuning. LoRA-fine-tuned LLaMA models outperform prompt-engineered GPT models, achieving a new state-of-the-art Valence CCC of 0.7822, and ablation studies show that textual audio descriptions significantly benefit smaller models. "whyItMatters":"The study demonstrates that domain adaptation through fine-tuning can surpass larger GPT models in multimodal emotion evaluation, highlighting the importance of tailored training for emotion recognition tasks."
The paper introduces UPHELD, a large benchmark of human-to-human dialogues written by professional script writers, featuring realistic turn densities and over 36,000 per-turn human annotations. It evaluates existing automatic metrics and LLM-as-a-judge methods, finding them unreliable against expert human judgment. Using UPHELD, the authors develop a Mixture-of-Judges framework that improves correlation with human assessments by about 30%.
arXiv:2607. 27816v2 Announce Type: replace-cross Abstract: Role-playing agents (RPAs) have become one of the most important consumer applications of large language models.
The paper introduces REFLECT, a role-aware transformer that integrates Semantic Role Labeling (SRL) configurations into self‑attention to better detect sentiment, emotion, politeness, and formality (SEPF) in dialogue. By fusing role-aware representations with contextual embeddings through a learned gate, REFLECT achieves significant improvements over BERT baselines on emotion, politeness, and formality tasks, especially for low‑frequency and complex cases, while sentiment gains are minimal. The approach also enhances interpretability and generalizes to real‑world mental‑health data.