New Technique Enhances LLMs’ Reasoning by Disregarding Irrelevant Data

November 29, 2023
New Technique Enhances LLMs’ Reasoning by Disregarding Irrelevant Data

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Large language models (LLM) have been making significant strides in various domains, yet their ability to reason effectively remains a subject of ongoing research. Several studies have explored different prompting techniques to enhance the logical problem-solving capabilities of LLMs.

The latest technique from researchers at Meta, named System 2 Attention (S2A), borrows concepts from psychological research. S2A meticulously revises the user’s prompt, eliminating any misleading or irrelevant information. By focusing solely on the task-relevant data, S2A allows LLMs to perform more accurately in question-answering and reasoning tasks.

Initial experiments indicate a notable improvement in the performance of language models using S2A, which can be useful for applications that require reliable reasoning capabilities.

LLMs and reasoning

The performance of LLMs in reasoning is a mixed bag. While certain prompt engineering techniques can enhance their performance, these models can falter when the prompt includes irrelevant or opinionated information. For instance, if a user’s question contains their personal guess or opinion, the model is prone to merely confirm or echo the user’s input rather than providing the correct answer.

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Anika Patel

Anika holds a Ph.D. in Anthropology from the University of Michigan and specializes in subcultures and fandom communities. She explores the intersection of technology and culture in her pieces for Hypernova.

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