MIMDE: Exploring the Use of Synthetic vs Human Data for Evaluating Multi-Insight Multi-Document Extraction Tasks

TL;DR

This paper introduces MIMDE, a new evaluation framework for multi-insight multi-document extraction tasks, comparing human and synthetic datasets to assess LLM performance and highlighting the limitations of synthetic data.

Contribution

The paper develops a novel evaluation framework for MIMDE tasks and provides a comprehensive benchmark of 20 LLMs using both synthetic and human datasets.

Findings

Strong correlation (0.71) between insights extracted from both datasets

Synthetic data does not fully capture the complexity of document-level analysis

Guidance on the potential and limitations of synthetic data for LLM evaluation

Abstract

Large language models (LLMs) have demonstrated remarkable capabilities in text analysis tasks, yet their evaluation on complex, real-world applications remains challenging. We define a set of tasks, Multi-Insight Multi-Document Extraction (MIMDE) tasks, which involves extracting an optimal set of insights from a document corpus and mapping these insights back to their source documents. This task is fundamental to many practical applications, from analyzing survey responses to processing medical records, where identifying and tracing key insights across documents is crucial. We develop an evaluation framework for MIMDE and introduce a novel set of complementary human and synthetic datasets to examine the potential of synthetic data for LLM evaluation. After establishing optimal metrics for comparing extracted insights, we benchmark 20 state-of-the-art LLMs on both datasets. Our analysis reveals a strong correlation (0.71) between the ability of LLMs to extracts insights on our two datasets but synthetic data fails to capture the complexity of document-level analysis. These findings offer crucial guidance for the use of synthetic data in evaluating text analysis systems, highlighting both its potential and limitations.