眼泪还是欢呼？通过文化诱发的差异情感反应基准测试LLMs
Tears or Cheers? Benchmarking LLMs via Culturally Elicited Distinct Affective Responses
AI总结：
本文提出CEDAR基准，通过文化诱发的差异情感反应评估LLMs，揭示语言一致性和文化契合度之间的脱节，展示文化根植的情感理解仍是模型的重大挑战。
摘要：
文化在人类情感处理中起着基础性决定作用，并深刻塑造了个体感知和解释情感刺激的方式。尽管这种内在联系，现有对大型语言模型中文化契合度的评估主要优先考虑陈述性知识，如地理事实或已建立的社会习俗。这些基准仍然不足以捕捉因多样化社会文化视角而固有的主观解释差异。为了解决这一限制，我们引入了CEDAR，一个完全由捕捉文化诱发的差异情感反应的场景构建的多模态基准。为构建CEDAR，我们实施了一种新的流程，利用LLM生成的临时标签来隔离产生跨文化情感差异的实例，并随后通过严格的人类评估得出可靠的地面真实注释。所得到的基准包含7种语言和14种细粒度情绪类别中的10,962个实例，每种语言包括400个多模态样本和1,166个纯文本样本。对17个代表性多语言模型的全面评估揭示了语言一致性和文化契合度之间的脱节，表明文化根基的情感理解仍然是当前模型的重大挑战。
英文摘要：
Culture serves as a fundamental determinant of human affective processing and profoundly shapes how individuals perceive and interpret emotional stimuli. Despite this intrinsic link extant evaluations regarding cultural alignment within Large Language Models primarily prioritize declarative knowledge such as geographical facts or established societal customs. These benchmarks remain insufficient to capture the subjective interpretative variance inherent to diverse sociocultural lenses. To address this limitation, we introduce CEDAR, a multimodal benchmark constructed entirely from scenarios capturing Culturally \underline{\textsc{E}}licited \underline{\textsc{D}}istinct \underline{\textsc{A}}ffective \underline{\textsc{R}}esponses. To construct CEDAR, we implement a novel pipeline that leverages LLM-generated provisional labels to isolate instances yielding cross-cultural emotional distinctions, and subsequently derives reliable ground-truth annotations through rigorous human evaluation. The resulting benchmark comprises 10,962 instances across seven languages and 14 fine-grained emotion categories, with each language including 400 multimodal and 1,166 text-only samples. Comprehensive evaluations of 17 representative multilingual models reveal a dissociation between language consistency and cultural alignment, demonstrating that culturally grounded affective understanding remains a significant challenge for current models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
