用大模型预测人类痛苦程度，还能通过游戏化测试动态评估其情感推理能力。
Leveraging Large Language Models for Predictive Analysis of Human Misery
- 将语言描述转化为0~100的痛苦评分，采用少样本提示提升预测效果。
- 少样本方法显著优于零样本，证明上下文示例对情感判断的关键作用。
- 创新设计'痛苦游戏秀'，可测试模型在反馈中调整推理的能力。
本研究探讨大型语言模型（LLMs）从自然语言描述的真实场景中预测人类感知痛苦值的能力。任务被建模为回归问题，模型需为每条输入语句分配0到100之间的标量分数。我们评估了多种提示策略，包括零样本、固定上下文少样本提示以及基于BERT句子嵌入的检索提示。少样本方法始终优于零样本基线，凸显上下文示例在情感预测中的价值。为突破静态评估局限，我们提出“痛苦游戏秀”——一种受电视节目启发的新型游戏化框架，包含序数比较、二分类、标量估计和反馈驱动推理等结构化环节。该框架不仅评估预测准确性，还检验模型根据纠正反馈进行适应性推理的能力。结果表明，LLM在动态情感推理任务中具有更广泛潜力。代码与数据链接：https://github.com/abhi1nandy2/Misery_Data_Exps_GitHub
原文摘要 · Abstract (English)
This study investigates the use of Large Language Models (LLMs) for predicting human-perceived misery scores from natural language descriptions of real-world scenarios. The task is framed as a regression problem, where the model assigns a scalar value from 0 to 100 to each input statement. We evaluate multiple prompting strategies, including zero-shot, fixed-context few-shot, and retrieval-based prompting using BERT sentence embeddings. Few-shot approaches consistently outperform zero-shot baselines, underscoring the value of contextual examples in affective prediction. To move beyond static evaluation, we introduce the "Misery Game Show", a novel gamified framework inspired by a television format. It tests LLMs through structured rounds involving ordinal comparison, binary classification, scalar estimation, and feedback-driven reasoning. This setup enables us to assess not only predictive accuracy but also the model's ability to adapt based on corrective feedback. The gamified evaluation highlights the broader potential of LLMs in dynamic emotional reasoning tasks beyond standard regression. Code and data link: https://github.com/abhi1nandy2/Misery_Data_Exps_GitHub
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