English(EN) Language Carries the Expert's Impression: Instrument-Anchored LLM Judges Transfer Counseling-Quality Assessment and Beat In-Domain Training

LLM通过跨领域转移在咨询质量评估方面表现出色

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-07 04:00

研究人员开发了一种使用大型语言模型（LLM）评估咨询对话中沟通质量的方法。通过在不同领域的数据上训练LLM，他们发现专家印象预测的跨领域转移优于领域内训练，Spearman相关系数分别为0.54和0.48。此预测的关键特征是LLM分析说话者文本时得出的构建分数，特别是与专家评分工具相关的分数。研究还强调了数据质量的影响，指出每位说话者的音频丢失和错误的说话者分割会负面影响评估。 AI

影响这项研究展示了LLM在咨询评估等专业领域的潜力，表明通过跨领域转移学习可以提高准确性。

排序理由该集群包含一篇在arXiv上发表的研究论文，详细介绍了一种新颖的LLM应用方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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arXiv cs.CL TIER_1 English(EN) · Tobias Hallmen, Elisabeth Andr\'e · 2026-10-07 04:00

语言承载专家印象：基于模型的LLM评判转移咨询质量评估并超越领域内训练

arXiv:2610.08055v1 Announce Type: new Abstract: Automatic assessment of communication quality in dyadic counseling conversations is bottlenecked by data: expert-rated corpora are small and expensive to grow. We study cross-domain transfer of expert overall-impression prediction a…

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报道来源 [1]

语言承载专家印象：基于模型的LLM评判转移咨询质量评估并超越领域内训练

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