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Poster
Large Language Models Can Be Easily Distracted by Irrelevant Context
Haoyue Shi · Xinyun Chen · Kanishka Misra · Nathan Scales · David Dohan · Ed Chi · Nathanael Schärli · Denny Zhou

Wed Jul 26 02:00 PM -- 03:30 PM (PDT) @ Exhibit Hall 1 #520
in Poster Session 3 »

Large language models have achieved impressive performance on various natural language processing tasks. However, so far they have been evaluated primarily on benchmarks where all information in the input context is relevant for solving the task. In this work, we investigate the distractibility of large language models, i.e., how the model prediction can be distracted by irrelevant context. In particular, we introduce Grade-School Math with Irrelevant Context (GSM-IC), an arithmetic reasoning dataset with irrelevant information in the problem description. We use this benchmark to measure the distractibility of different prompting techniques for large language models, and find that the model is easily distracted by irrelevant information. We also identify several approaches for mitigating this deficiency, such as decoding with self-consistency and adding to the prompt an instruction that tells the language model to ignore the irrelevant information.

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Author Information

Haoyue Shi (Toyota Technological Institute at Chicago)
Xinyun Chen (Google DeepMind)
Kanishka Misra (Purdue University)
Nathan Scales (Google DeepMind)
David Dohan (OpenAI)
Ed Chi (Google)
Nathanael Schärli (Research, Google)
Denny Zhou (Google Brain)

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