Leveraging Large Language Models for Predictive Analysis of Human Misery

TL;DR

This paper explores using Large Language Models to predict human misery levels from text, comparing prompting strategies and introducing a gamified evaluation framework to assess adaptive emotional reasoning.

Contribution

It introduces a novel gamified testing framework for LLMs and demonstrates the effectiveness of few-shot prompting in affective prediction tasks.

Findings

Few-shot prompting outperforms zero-shot approaches.

The 'Misery Game Show' evaluates model adaptability and reasoning.

LLMs show promise in dynamic emotional understanding.

Abstract

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