TACT: Advancing Complex Aggregative Reasoning with Information Extraction Tools

TL;DR

This paper introduces TACT, a challenging dataset for evaluating LLMs' complex reasoning and calculation abilities across texts, revealing current models' limitations and proposing a tool-based framework to improve performance.

Contribution

The paper presents TACT, a novel dataset for complex aggregative reasoning, and proposes a tool-based modeling framework to enhance LLMs' reasoning and computational skills.

Findings

LLMs perform poorly on TACT with accuracy below 38%

Current models struggle with table-generation, command-generation, and execution components

Tool-based prompting improves model performance over standard techniques

Abstract

Large Language Models (LLMs) often do not perform well on queries that require the aggregation of information across texts. To better evaluate this setting and facilitate modeling efforts, we introduce TACT - Text And Calculations through Tables, a dataset crafted to evaluate LLMs' reasoning and computational abilities using complex instructions. TACT contains challenging instructions that demand stitching information scattered across one or more texts, and performing complex integration on this information to generate the answer. We construct this dataset by leveraging an existing dataset of texts and their associated tables. For each such tables, we formulate new queries, and gather their respective answers. We demonstrate that all contemporary LLMs perform poorly on this dataset, achieving an accuracy below 38%. To pinpoint the difficulties and thoroughly dissect the problem, we analyze model performance across three components: table-generation, Pandas command-generation, and execution. Unexpectedly, we discover that each component presents substantial challenges for current LLMs. These insights lead us to propose a focused modeling framework, which we refer to as IE as a tool. Specifically, we propose to add "tools" for each of the above steps, and implement each such tool with few-shot prompting. This approach shows an improvement over existing prompting techniques, offering a promising direction for enhancing model capabilities in these tasks.