[2024 Best AI Paper] Enhancing Robustness in Large Language Models: Prompting for Mitigating the Imp

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This video was created using https://paperspeech.com. If you’d like to create explainer videos for your own papers, please visit the website! Title: Enhancing Robustness in Large Language Models: Prompting for Mitigating the Impact of Irrelevant Information Authors: Ming Jiang, Tingting Huang, Biao Guo, Yao Lu, Feng Zhang Abstract: In recent years, Large language models (LLMs) have garnered significant attention due to their superior performance in complex reasoning tasks. However, recent studies may diminish their reasoning capabilities markedly when problem descriptions contain irrelevant information, even with the use of advanced prompting techniques. To further investigate this issue, a dataset of primary school mathematics problems containing irrelevant information, named GSMIR, was constructed. Testing prominent LLMs and prompting techniques on this dataset revealed that while LLMs can identify irrelevant information, they do not effectively mitigate the interference it causes