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This episode examines the limitations of Large Language Models (LLMs) when it comes to complex reasoning tasks. While LLMs excel at linguistic tasks, they often struggle with problems that require non-linear, multi-step deduction, a challenge highlighted in the article's newly introduced Non-Linear Reasoning (NLR) dataset. The episode explores a potential solution: neurosymbolic AI, a hybrid approach that combines the strengths of LLMs with the logical reasoning power of symbolic systems like Prolog.
Article: https://arxiv.org/abs/2407.11373
This episode examines the limitations of Large Language Models (LLMs) when it comes to complex reasoning tasks. While LLMs excel at linguistic tasks, they often struggle with problems that require non-linear, multi-step deduction, a challenge highlighted in the article's newly introduced Non-Linear Reasoning (NLR) dataset. The episode explores a potential solution: neurosymbolic AI, a hybrid approach that combines the strengths of LLMs with the logical reasoning power of symbolic systems like Prolog.
Article: https://arxiv.org/abs/2407.11373