April 10, 2024, 8:39 a.m. | /u/Gaussian_Kernel

Machine Learning www.reddit.com

Paper: https://www.arxiv.org/abs/2404.02255

Abstract: Despite demonstrating emergent reasoning abilities, Large Language Models (LLMS) often lose track of complex, multi-step reasoning. Existing studies show that providing guidance via decomposing the original question into multiple subproblems elicits more robustness in LLM reasoning -- a decomposer generates the subproblems, and a solver solves each of these subproblems. However, these techniques fail to accommodate coordination between the decomposer and the solver modules (either in a single model or different specialized ones) -- the decomposer does …

abstract guidance however language language models large language large language models llm llm reasoning llms machinelearning multiple question reasoning robustness show solver studies via

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