The Impact of Combining Large Language Models (LLMs) with External Tools
Practical Solutions and Value
Recent developments in Natural Language Processing (NLP) have seen large language models (LLMs) achieving human-level performance in various fields. However, their limitations in reasoning can be addressed by combining them with external tools and symbolic reasoning modules.
This combination has shown improvements in LLMs’ performance on various reasoning tasks, particularly in reducing arithmetic errors. Researchers at the University of California, Berkeley, have proposed integrating a reliable, deductive reasoning module into the LLM inference pipeline. This approach significantly enhances the LLMs’ performance for mathematical reasoning.
Additionally, a new dataset called the Non-Linear Reasoning dataset (NLR) has been introduced to test LLMs’ ability to handle mathematical reasoning. The NLR dataset aims to address issues found in existing datasets and provides unique constraint problems, math word problems, and algorithmic instruction problems for testing LLMs’ capabilities.
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