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