# Assessing the quality of information extraction

arXiv: https://arxiv.org/abs/2404.04068 · HTML: https://mlsanity.com/paper/2404.04068/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 10 references that are in the map.
- First posted on arXiv 5 Apr 2024.

## Builds on (most cited first, 4 of 4)

- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [Large Language Models for Generative Information Extraction: A Survey](https://mlsanity.com/paper/2312.17617/) (2023)
- [In Search of Needles in a 11M Haystack: Recurrent Memory Finds What LLMs Miss](https://mlsanity.com/paper/2402.10790/) (2024)
- [Making Large Language Models Better Data Creators](https://mlsanity.com/paper/2310.20111/) (2023)

## Built on by (most cited first, 2 of 2)

- [Hybrid OCR-LLM Framework for Enterprise-Scale Document Information Extraction Under Copy-heavy Task](https://mlsanity.com/paper/2510.10138/) (2025)
- [Climate AI for Corporate Decarbonization Metrics Extraction](https://mlsanity.com/paper/2411.03402/) (2024)

## Abstract (arXiv)

Advances in large language models have notably enhanced the efficiency of information extraction from unstructured and semi-structured data sources. As these technologies become integral to various applications, establishing an objective measure for the quality of information extraction becomes imperative. However, the scarcity of labeled data presents significant challenges to this endeavor. In this paper, we introduce an automatic framework to assess the quality of the information extraction/retrieval and its completeness. The framework focuses on information extraction in the form of entity and its properties. We discuss how to handle the input/output size limitations of the large language models and analyze their performance when extracting the information. In particular, we introduce scores to evaluate the quality of the extraction and provide an extensive discussion on how to interpret them.