How Easily do Irrelevant Inputs Skew the Responses of Large Language Models? This paper investigates how large language models (LLMs) are affected by irrelevant information, particularly when that information is semantically related to the question. The study presents a framework for generating high-quality irrelevant information, ranging from semantically unrelated to partially related and related to the question. The research shows that LLMs are more easily misled by semantically related irrelevant information, which is highly similar to the top-ranked information retrieved by existing systems. The study also finds that current methods for handling irrelevant information have limited effectiveness in improving the robustness of LLMs against such distractions. The paper evaluates the performance of LLMs under various conditions, including the quantity of irrelevant information, the format of the question, and the presence of relevant information. It finds that LLMs are more robust to irrelevant information when the question format is free-form compared to multiple-choice or boolean formats. Additionally, the study highlights that current strategies for improving LLMs' ability to identify and disregard irrelevant information have limited success, often resulting in only marginal improvements or even detrimental effects. The research also demonstrates that LLMs can be easily distracted by misleading information, even when that information is semantically related to the question. This can lead to incorrect answers and a loss of accuracy. The study provides a comprehensive analysis of how LLMs respond to different types of irrelevant information and highlights the challenges in developing robust LLMs that can effectively filter out such distractions. The paper concludes that current LLMs still struggle with distinguishing between highly semantically related irrelevant information and relevant information. This suggests that further research is needed to develop more robust LLMs that can effectively handle irrelevant information in real-world applications. The study provides valuable insights into the limitations of current LLMs and the challenges in improving their robustness to irrelevant information.How Easily do Irrelevant Inputs Skew the Responses of Large Language Models? This paper investigates how large language models (LLMs) are affected by irrelevant information, particularly when that information is semantically related to the question. The study presents a framework for generating high-quality irrelevant information, ranging from semantically unrelated to partially related and related to the question. The research shows that LLMs are more easily misled by semantically related irrelevant information, which is highly similar to the top-ranked information retrieved by existing systems. The study also finds that current methods for handling irrelevant information have limited effectiveness in improving the robustness of LLMs against such distractions. The paper evaluates the performance of LLMs under various conditions, including the quantity of irrelevant information, the format of the question, and the presence of relevant information. It finds that LLMs are more robust to irrelevant information when the question format is free-form compared to multiple-choice or boolean formats. Additionally, the study highlights that current strategies for improving LLMs' ability to identify and disregard irrelevant information have limited success, often resulting in only marginal improvements or even detrimental effects. The research also demonstrates that LLMs can be easily distracted by misleading information, even when that information is semantically related to the question. This can lead to incorrect answers and a loss of accuracy. The study provides a comprehensive analysis of how LLMs respond to different types of irrelevant information and highlights the challenges in developing robust LLMs that can effectively filter out such distractions. The paper concludes that current LLMs still struggle with distinguishing between highly semantically related irrelevant information and relevant information. This suggests that further research is needed to develop more robust LLMs that can effectively handle irrelevant information in real-world applications. The study provides valuable insights into the limitations of current LLMs and the challenges in improving their robustness to irrelevant information.