Intuitive or Dependent? Investigating LLms’ Robustness to Conflicting Prompts

Jiahao Ying, Yixin Cao, Kai Xiong, yidong He, Long Cui, Yongbin Liu

Keywords: LLM, Robustness, Evaluation Framework, dataset

TL;DR: How robustness are LLMs when facing the conflict

Abstract:

This paper explores the robustness of LLMs' preference to their internal memory or the given prompt, which may contain contrasting information in real-world applications due to noise or task settings.
To this end, we establish a quantitative benchmarking framework and conduct role playing intervention to control LLMs' preference.
In specific, we define two types of robustness, factual robustness targeting the ability in identifying the correct fact from prompts or memory, and decision style to categorize LLMs' behavior in making consistent choices --- assuming there is no definitive ``right" answer --- intuitive, dependent, or rational based on cognitive theory.
Our findings, derived from extensive experiments on seven open-source and closed-source LLMs, reveal that these models are highly susceptible to misleading prompts, especially for instructing commonsense knowledge. While detailed instructions can mitigate the selection of misleading answers, they also increase the incidence of invalid responses. After Unraveling the preference, we intervene different sized LLMs through specific style of role instruction, showing their varying upper bound of robustness and adaptivity.

Primary Area: visualization or interpretation of learned representations

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Submission Number: 5551