Confirming Our Biases? Evaluating the Capabilities, Risks, and Societal Impact of Large Language Models

2026-08-07

AI Summary

This study investigates whether large language models amplify biases present in user prompts and remain susceptible to prompt framing even on factual questions. By employing a controlled experimental design, the authors construct 160 prompts spanning ten topics to systematically disentangle the effects of implicit prompt framing from explicit manipulation on model outputs. The evaluation across six prominent large language models reveals a consistent tendency for models to align their responses with the framing of the prompt, often prioritizing user suggestions over factual consistency—even when objective facts are unambiguous. This work provides the first empirical evidence of the vulnerability of large language models to bias induction in factual domains, highlighting a critical limitation in their reliability despite advances in scale and training.

Abstract

It is well established that large language models (LLMs) are sensitive to prompt framing, reflecting patterns in their training data or prior prompts. In this study, we investigate the extent to which LLMs reinforce users biases expressed in the prompts and examine the boundary between implicit framing effects and explicit prompt manipulation. Specifically, we evaluate how susceptible LLMs are to direct and suggestive prompts that encourage models to support or challenge particular positions. We evaluate six LLMs using 160 distinct prompts spanning ten topics across opinion-based and factual domains. The prompts systematically vary in prompting strategy, support versus challenge instructions, prompt polarity, users' expressed beliefs, and topic domain, spanning both opinion-based and factual questions. Our results show that LLMs systematically adapt their responses to align with prompt framing, even in factual contexts. This suggests that prompt framing can outweigh factual consistency in model responses. Overall, our findings delineate the extent and boundaries of LLM manipulability. Furthermore, the results imply that LLMs can reinforce subtle user biases and are susceptible to explicit prompt manipulation even in domains where responses should remain factually stable.

Problem

• large language models
• bias reinforcement
• prompt framing
• factual consistency
• manipulability

Innovation

• prompt framing
• bias reinforcement
• large language models
• factual consistency
• prompt manipulation