Summary of Uda: a Benchmark Suite For Retrieval Augmented Generation in Real-world Document Analysis, by Yulong Hui et al.
UDA: A Benchmark Suite for Retrieval Augmented Generation in Real-world Document Analysis
by Yulong Hui, Yao Lu, Huanchen Zhang
First submitted to arxiv on: 21 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a benchmark suite for evaluating Large Language Models (LLMs) in real-world document analysis tasks. The Unstructured Document Analysis (UDA) benchmark includes 2,965 real-world documents and 29,590 expert-annotated Q&A pairs. The authors revisit popular LLM- and RAG-based solutions for document analysis, evaluating design choices and answer qualities across multiple document domains and diverse query types. The evaluation yields interesting findings and highlights the importance of data parsing and retrieval. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making computers better at understanding and working with documents. Right now, big language models are good at answering questions when they have lots of data to work with. But what happens when that data is in a format like HTML or PDF? It can be really long and hard for the computer to understand. To fix this problem, the authors created a test suite called Unstructured Document Analysis (UDA) that includes 2,965 real-world documents and 29,590 expert-annotated Q&A pairs. |
Keywords
» Artificial intelligence » Parsing » Rag
