Leveraging Large Language Models for Predictive Analysis of Human Misery

2025/08/18 by Seal, Bishanka, Seetharaman, Rahul, Aman Bansal +3
Computer Science · Social Sciences · #Computation and Language (cs.CL) #Computers and Society (cs.CY) #FOS: Computer and information sciences #Hate Speech and Cyberbullying Detection #Misinformation and Its Impacts

paper · pdf · doi:10.48550/arxiv.2508.12669

openalex publication_date 2025/08/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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

Citations

• GPT-4o System Card
• Fine-grained Affective Processing Capabilities Emerging from Large Language Models
• Sparks of Artificial General Intelligence: Early experiments with GPT-4
• A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Models
• GPT-4 Technical Report
• BNAI, NO-TOKEN, and MIND-UNITY: Pillars of a Systemic Revolution in Artificial Intelligence
• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

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