Too Big to Fool: Resisting Deception in Language Models
Keywords: Large Language Models, Evaluation, Misinformation, In-Context Learning, World Models, Reasoning
TL;DR: This paper introduces a powerful evaluation method showing that larger language models are more resilient to misleading prompts and better at using truthful in-context hints.
Abstract: Large language models must balance their weight-encoded knowledge with in-context information from prompts to generate accurate responses. This paper investigates this interplay by analyzing how models of varying capacities within the same family handle intentionally misleading in-context information. Our experiments demonstrate that larger models exhibit higher resilience to deceptive prompts, showcasing an advanced ability to interpret and integrate prompt information with their internal knowledge. Furthermore, we find that larger models outperform smaller ones in following legitimate instructions, indicating that their resilience is not due to disregarding in-context information. We also show that this phenomenon is likely not a result of memorization but stems from the models' ability to better leverage implicit task-relevant information from the prompt alongside their internally stored knowledge.
Primary Area: foundation or frontier models, including LLMs
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Submission Number: 7458
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