The study investigates how LLMs handle complex mathematical tasks in semi-structured tables, introducing a novel prompting technique that outperforms other baselines. The analysis covers errors in extraction, reasoning, and calculation, providing insights into model performance across different question types and reasoning steps.
The research delves into the limitations of LLMs when dealing with numerical reasoning over semi-structured data, highlighting challenges such as domain-specific knowledge requirements and multi-step reasoning difficulties. The study also outlines future directions for exploring computational models that excel in numerical reasoning tasks across various domains beyond finance.
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by Pragya Sriva... a las arxiv.org 03-01-2024
https://arxiv.org/pdf/2402.11194.pdfConsultas más profundas