ESC-Judge: A Framework for Comparing Emotional Support Conversational Agents
- URL: http://arxiv.org/abs/2505.12531v1
- Date: Sun, 18 May 2025 20:04:59 GMT
- Title: ESC-Judge: A Framework for Comparing Emotional Support Conversational Agents
- Abstract summary: We present ESC-Judge, the first end-to-end evaluation framework for large language models (LLMs)<n> ESC-Judge grounds head-to-head comparisons of emotional-support LLMs in Clara Hill's established Exploration-Insight-Action counseling model.<n>All code, prompts, synthetic roles, transcripts, and judgment scripts are released to promote transparent progress in emotionally supportive AI.
- Score: 2.3020018305241337
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Large language models (LLMs) increasingly power mental-health chatbots, yet the field still lacks a scalable, theory-grounded way to decide which model is most effective to deploy. We present ESC-Judge, the first end-to-end evaluation framework that (i) grounds head-to-head comparisons of emotional-support LLMs in Clara Hill's established Exploration-Insight-Action counseling model, providing a structured and interpretable view of performance, and (ii) fully automates the evaluation pipeline at scale. ESC-Judge operates in three stages: first, it synthesizes realistic help-seeker roles by sampling empirically salient attributes such as stressors, personality, and life history; second, it has two candidate support agents conduct separate sessions with the same role, isolating model-specific strategies; and third, it asks a specialized judge LLM to express pairwise preferences across rubric-anchored skills that span the Exploration, Insight, and Action spectrum. In our study, ESC-Judge matched PhD-level annotators on 85 percent of Exploration, 83 percent of Insight, and 86 percent of Action decisions, demonstrating human-level reliability at a fraction of the cost. All code, prompts, synthetic roles, transcripts, and judgment scripts are released to promote transparent progress in emotionally supportive AI.
Related papers
- EmoS: A Theory-Grounded Framework for Evaluating and Aligning Emotional Intelligence in Spoken Language Models [66.28519000191191]
EmoSBench is the first comprehensive EI evaluation benchmark for Spoken Language Models.<n>EmoS reaches 83.8% accuracy, approaching human-level performance.
arXiv Detail & Related papers (2026-08-10T06:58:30Z) - SoCRATES: Towards Reliable Automated Evaluation of Proactive LLM Mediation across Domains and Socio-cognitive Variations [22.051136282030544]
SoCRATES is a benchmark for evaluating proactive LLM mediators in realistic, multi-domain testbeds.<n>It constructs scenarios from real conflicts through an agentic pipeline across eight domains.<n>It probes five socio-cognitive adaptation axes (strategic posture, party composition, history length, emotional reactivity, and cultural identity) and scores each topic only on the turns that advance it via a topic-localized evaluator.
arXiv Detail & Related papers (2026-06-04T01:19:40Z) - SAVOIR: Learning Social Savoir-Faire via Shapley-based Reward Attribution [82.31558282651811]
Social intelligence, the ability to navigate complex interpersonal interactions, presents a fundamental challenge for language agents.<n>Existing approaches directly employ language models to distribute episode-level rewards, yielding attributions that are retrospective and lack theoretical grounding.<n>We propose SAVOIR, a novel principled framework grounded in cooperative game theory.
arXiv Detail & Related papers (2026-04-21T02:08:25Z) - Interactive ASR: Towards Human-Like Interaction and Semantic Coherence Evaluation for Agentic Speech Recognition [52.63676763985825]
We propose a semantic-aware evaluation metric to assess recognition quality beyond token-level accuracy.<n>We design an agent framework to simulate human-like multi-turn interaction, enabling iterative refinement of recognition outputs.<n>We will release the code to facilitate future research in interactive and agentic ASR.
arXiv Detail & Related papers (2026-04-10T09:02:42Z) - MERRY: Semantically Decoupled Evaluation of Multimodal Emotional and Role Consistencies of Role-Playing Agents [41.829135334587626]
MERRY is a semantically decoupled evaluation framework for assessing Multimodal Emotional and Role consistencies of Role-playing agents.<n>We transform the traditional subjective scoring approach into a novel bidirectional-evidence-finding task.<n>We conduct extensive evaluations based on MERRY.
arXiv Detail & Related papers (2026-02-24T02:53:58Z) - Beyond Description: Cognitively Benchmarking Fine-Grained Action for Embodied Agents [52.14392337070763]
We introduce CFG-Bench, a new benchmark designed to systematically evaluate fine-grained action intelligence.<n>CFG-Bench consists of 1,368 curated videos paired with 19,562 three-modalities question-answer pairs targeting four cognitive abilities.<n>Our comprehensive evaluation on CFG-Bench reveals that leading MLLMs struggle to produce detailed instructions for physical interactions.
arXiv Detail & Related papers (2025-11-24T02:02:29Z) - SageLM: A Multi-aspect and Explainable Large Language Model for Speech Judgement [74.51476422119457]
Speech-to-Speech (S2S) Large Language Models (LLMs) are foundational to natural human-computer interaction.<n>We propose textttSageLM, an end-to-end, multi-aspect, and explainable speech LLM for comprehensive S2S LLMs evaluation.
arXiv Detail & Related papers (2025-08-28T15:47:37Z) - DICE: Dynamic In-Context Example Selection in LLM Agents via Efficient Knowledge Transfer [50.64531021352504]
Large language model-based agents, empowered by in-context learning (ICL), have demonstrated strong capabilities in complex reasoning and tool-use tasks.<n>Existing approaches typically rely on example selection, including in some agentic or multi-step settings.<n>We propose DICE, a theoretically grounded ICL framework for agentic tasks that selects the most relevant demonstrations at each step of reasoning.
arXiv Detail & Related papers (2025-07-31T13:42:14Z) - Towards a Cascaded LLM Framework for Cost-effective Human-AI Decision-Making [55.2480439325792]
We present a cascaded LLM decision framework that adaptively delegates tasks across multiple tiers of expertise.<n>First, a deferral policy determines whether to accept the base model's answer or regenerate it with the large model.<n>Second, an abstention policy decides whether the cascade model response is sufficiently certain or requires human intervention.
arXiv Detail & Related papers (2025-06-13T15:36:22Z) - The Pragmatic Mind of Machines: Tracing the Emergence of Pragmatic Competence in Large Language Models [6.187227278086245]
Large language models (LLMs) have demonstrated emerging capabilities in social intelligence, including implicature resolution and theory-of-mind reasoning.<n>In this work, we evaluate whether LLMs at different training stages can accurately infer speaker intentions.<n>We systematically evaluate 22 LLMs across 3 key training stages: after pre-training, supervised fine-tuning (SFT), and preference optimization.
arXiv Detail & Related papers (2025-05-24T04:24:59Z) - Ψ-Arena: Interactive Assessment and Optimization of LLM-based Psychological Counselors with Tripartite Feedback [51.26493826461026]
We propose Psi-Arena, an interactive framework for comprehensive assessment and optimization of large language models (LLMs)<n>Arena features realistic arena interactions that simulate real-world counseling through multi-stage dialogues with psychologically profiled NPC clients.<n>Experiments across eight state-of-the-art LLMs show significant performance variations in different real-world scenarios and evaluation perspectives.
arXiv Detail & Related papers (2025-05-06T08:22:51Z) - A Survey of Frontiers in LLM Reasoning: Inference Scaling, Learning to Reason, and Agentic Systems [93.8285345915925]
Reasoning is a fundamental cognitive process that enables logical inference, problem-solving, and decision-making.<n>With the rapid advancement of large language models (LLMs), reasoning has emerged as a key capability that distinguishes advanced AI systems.<n>We categorize existing methods along two dimensions: (1) Regimes, which define the stage at which reasoning is achieved; and (2) Architectures, which determine the components involved in the reasoning process.
arXiv Detail & Related papers (2025-04-12T01:27:49Z) - EssayJudge: A Multi-Granular Benchmark for Assessing Automated Essay Scoring Capabilities of Multimodal Large Language Models [19.271790170055375]
EssayJudge offers precise, context-rich evaluations without manual feature engineering, addressing longstanding AES limitations.<n>Our experiments with 18 representative MLLMs reveal gaps in AES performance compared to human evaluation, particularly in discourse-level traits.
arXiv Detail & Related papers (2025-02-17T15:31:59Z) - Reflection-Bench: Evaluating Epistemic Agency in Large Language Models [10.801745760525838]
Epistemic agency is the ability to flexibly construct, adapt, and monitor beliefs about dynamic environments.<n>We propose Reflection-Bench, a benchmark consisting of seven tasks with long-term relevance and minimization of data leakage.<n>Our findings suggest several promising research directions, including enhancing core cognitive functions, improving cross-functional coordination, and developing adaptive processing mechanisms.
arXiv Detail & Related papers (2024-10-21T17:59:50Z) - ESC-Eval: Evaluating Emotion Support Conversations in Large Language Models [55.301188787490545]
Emotion Support Conversation (ESC) aims to reduce human stress, offer emotional guidance, and enhance human mental and physical well-being.
We propose an ESC Evaluation framework (ESC-Eval), which uses a role-playing agent to interact with ESC models.
We conduct comprehensive human annotations on interactive multi-turn dialogues of different ESC models.
arXiv Detail & Related papers (2024-06-21T08:03:33Z) - FEEL: A Framework for Evaluating Emotional Support Capability with Large Language Models [14.894922829587841]
Emotional Support Conversation (ESC) is a typical dialogue that can effectively assist the user in mitigating emotional pressures.
Current non-artificial methodologies face challenges in effectively appraising the emotional support capability.
We propose a novel model FEEL, employing Large Language Models (LLMs) as evaluators to assess emotional support capabilities.
arXiv Detail & Related papers (2024-03-23T03:32:26Z) - AgentBoard: An Analytical Evaluation Board of Multi-turn LLM Agents [74.16170899755281]
We introduce AgentBoard, a pioneering comprehensive benchmark and accompanied open-source evaluation framework tailored to analytical evaluation of LLM agents.<n>AgentBoard offers a fine-grained progress rate metric that captures incremental advancements as well as a comprehensive evaluation toolkit.<n>This not only sheds light on the capabilities and limitations of LLM agents but also propels the interpretability of their performance to the forefront.
arXiv Detail & Related papers (2024-01-24T01:51:00Z) - Better Zero-Shot Reasoning with Role-Play Prompting [10.90357246745529]
Role-play prompting consistently surpasses the standard zero-shot approach across most datasets.
This highlights its potential to augment the reasoning capabilities of large language models.
arXiv Detail & Related papers (2023-08-15T11:08:30Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.