Summary Language Model-Based Document Information Extraction and Localization arxiv.org
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One Line
LMDX utilizes LLMs to successfully extract entities from VRDs, addressing issues with semi-structured documents and achieving impressive accuracy in extracting diverse entity types.
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Slide Presentation (9 slides)
Key Points
- LMDX is a methodology that uses Large Language Models (LLMs) to extract key entities from visually rich documents (VRDs) with high precision and localization.
- LMDX addresses the challenge of understanding complex layouts and tabular arrangements in semi-structured document information extraction.
- The LMDX pipeline consists of five stages: OCR, chunking, prompt generation, LLM inference, and decoding.
- LMDX PaLM 2-S, which uses the PaLM 2-S LLM, achieves state-of-the-art results on the VRDU and CORD benchmarks.
- Ablation studies show the importance of base entity extraction training, coordinate tokens, and sampling strategy in LMDX methodology.
- Error analysis reveals common error patterns in LMDX caused by OCR lines grouping multiple segments incorrectly.
- LMDX combines the power of language models with traditional document analysis techniques for information extraction and localization.
- LMDX outperforms baselines in terms of accuracy and performance, demonstrating the potential of language model-based approaches in document analysis.
Summaries
25 word summary
LMDX uses LLMs to extract entities from VRDs, overcoming challenges with semi-structured documents. It achieves state-of-the-art results and can accurately extract various types of entities.
85 word summary
LMDX utilizes Large Language Models (LLMs) to accurately extract key entities from visually rich documents (VRDs). It overcomes the challenge of extracting information from semi-structured documents by incorporating layout encoding and a grounding mechanism within LLMs. The LMDX pipeline consists of five stages, achieving state-of-the-art results on benchmarks. Error analysis identifies OCR line grouping as a common error pattern, with potential solutions including incorporating the image modality. LMDX is highly effective in accurately extracting singular, repeated, and hierarchical entities from visually rich documents using LLMs.
127 word summary
LMDX is a methodology that uses Large Language Models (LLMs) to extract key entities from visually rich documents (VRDs) with high precision and localization. It addresses the challenge of semi-structured document information extraction by introducing layout encoding within LLMs and a grounding mechanism. The LMDX pipeline consists of five stages: OCR, chunking, prompt generation, LLM inference, and decoding. LMDX PaLM 2-S achieves state-of-the-art results on the VRDU and CORD benchmarks, outperforming existing baselines. Error analysis reveals that a common error pattern in LMDX is caused by OCR lines grouping multiple semantically different segments. Incorporating the image modality is identified as a potential solution. LMDX is a highly effective methodology for extracting information from visually rich documents using LLMs, achieving accurate extraction of singular, repeated, and hierarchical entities.
404 word summary
LMDX (Language Model-Based Document Information Extraction and Localization) is a methodology that uses Large Language Models (LLMs) to extract key entities from visually rich documents (VRDs) with high precision and localization. It addresses the challenge of semi-structured document information extraction by introducing layout encoding within LLMs and a grounding mechanism. LMDX can extract singular, repeated, and hierarchical entities with or without training data while localizing them within the document.
The LMDX pipeline consists of five stages: Optical Character Recognition (OCR), chunking, prompt generation, LLM inference, and decoding. OCR obtains words and line segments from the document image. Chunking divides the document into smaller chunks for LLM processing. Prompt generation creates LLM prompts for each chunk, including document representation, task description, and schema representation. LLM inference involves running the LLM with the prompts and sampling multiple completions. Decoding parses the LLM completions into structured entities and their locations.
LMDX PaLM 2-S, which uses the PaLM 2-S LLM, achieves state-of-the-art results on the Visually Rich Document Understanding (VRDU) and Consolidated Receipt Dataset (CORD) benchmarks. It outperforms existing baselines, demonstrating data efficiency by achieving similar extraction quality at zero-shot as baselines with 10-100 training documents. Ablation studies show that the base entity extraction training stage is crucial for extraction quality, especially in few-shot scenarios and zero-shot extraction.
Error analysis reveals that a common error pattern in LMDX is caused by OCR lines grouping multiple semantically different segments, leading to incorrect associations in predictions. Incorporating the image modality is identified as a potential solution.
In conclusion, LMDX is a highly effective methodology for extracting information from visually rich documents using LLMs. It achieves state-of-the-art results, demonstrates data efficiency, and enables accurate extraction of singular, repeated, and hierarchical entities. The paper discusses the use of language models for extracting and localizing information from documents. The authors propose a method called LMDX that combines language models with traditional document analysis techniques.
LMDX outperforms two baselines, LayoutLMv3 LARGE and Donut, on three benchmark datasets. It achieves higher accuracy in extracting entities and localizing information. The success of LMDX is attributed to the use of language models, which can learn complex patterns and relationships in documents.
Ablation studies show that both document chunking and entity value parsing contribute significantly to the performance of LMDX. In conclusion, LMDX is an effective framework for document information extraction and localization. It outperforms existing baselines and has the potential to improve document analysis and understanding tasks.
528 word summary
LMDX (Language Model-Based Document Information Extraction and Localization) is a methodology that utilizes Large Language Models (LLMs) to extract key entities from visually rich documents (VRDs) with high precision and localization. LMDX addresses the challenge of semi-structured document information extraction by introducing layout encoding within LLMs and a grounding mechanism. It can extract singular, repeated, and hierarchical entities with or without training data while localizing them within the document.
The LMDX pipeline consists of five stages: Optical Character Recognition (OCR), chunking, prompt generation, LLM inference, and decoding. OCR obtains words and line segments from the document image. Chunking divides the document into smaller chunks for LLM processing. Prompt generation creates LLM prompts for each chunk, including document representation, task description, and schema representation. LLM inference involves running the LLM with the prompts and sampling multiple completions. Decoding parses the LLM completions into structured entities and their locations.
LMDX PaLM 2-S, which uses the PaLM 2-S LLM, achieves state-of-the-art results on the Visually Rich Document Understanding (VRDU) and Consolidated Receipt Dataset (CORD) benchmarks. It outperforms existing baselines, demonstrating data efficiency by achieving similar extraction quality at zero-shot as baselines with 10-100 training documents. LMDX PaLM 2-S also generalizes well to unseen templates, particularly for hierarchical entities like line items.
Ablation studies show that the base entity extraction training stage is crucial for extraction quality, especially in few-shot scenarios and zero-shot extraction. Removing coordinate tokens leads to a substantial decrease in extraction quality, while removing the sampling strategy slightly reduces extraction quality but removes error correction capability.
Error analysis reveals that a common error pattern in LMDX is caused by OCR lines grouping multiple semantically different segments, leading to incorrect associations in predictions. Incorporating the image modality is identified as a potential solution.
In conclusion, LMDX is a highly effective methodology for extracting information from visually rich documents using LLMs. It achieves state-of-the-art results, demonstrates data efficiency, and enables accurate extraction of singular, repeated, and hierarchical entities. Future work includes incorporating the image modality and exploring open-source LLMs for further improvements.
The paper discusses the use of language models for extracting and localizing information from documents. The authors propose a method called LMDX that combines language models with traditional document analysis techniques.
The LMDX framework consists of three main components: document chunking, entity value parsing, and responses decoding. Document chunking divides the document into smaller segments based on token limits. Entity value parsing extracts values for specific entities using regular expressions and segment mappings. Responses decoding decodes the extracted entities from multiple language model responses using majority voting.
LMDX outperforms two baselines, LayoutLMv3 LARGE and Donut, on three benchmark datasets. It achieves higher accuracy in extracting entities and localizing information. The success of LMDX is attributed to the use of language models, which can learn complex patterns and relationships in documents.
Ablation studies show that both document chunking and entity value parsing contribute significantly to the performance of LMDX. The model can interpret the layout of the document without explicit coordinate information.
In conclusion, LMDX is an effective framework for document information extraction and localization. It outperforms existing baselines and has the potential to improve document analysis and understanding tasks.
792 word summary
LMDX (Language Model-Based Document Information Extraction and Localization) is a methodology that leverages Large Language Models (LLMs) for extracting key entities from visually rich documents (VRDs) with high precision and localization. LLMs have shown significant progress in Natural Language Processing (NLP) but have not been successfully applied to semi-structured document information extraction, which requires understanding complex layouts and tabular arrangements. LMDX addresses this challenge by introducing layout encoding within LLMs and a grounding mechanism to ensure accurate extraction. It can extract singular, repeated, and hierarchical entities with or without training data while localizing them within the document.
The LMDX pipeline consists of five stages: Optical Character Recognition (OCR), chunking, prompt generation, LLM inference, and decoding. OCR is used to obtain words and lines segments from the document image. Chunking divides the document into smaller chunks for LLM processing. Prompt generation creates LLM prompts for each chunk, including document representation, task description, and schema representation. LLM inference involves running the LLM with the prompts and sampling multiple completions. Decoding parses the LLM completions into structured entities and their locations.
LMDX PaLM 2-S, which uses the PaLM 2-S LLM, is evaluated on the Visually Rich Document Understanding (VRDU) and Consolidated Receipt Dataset (CORD) benchmarks. It achieves state-of-the-art results on both benchmarks, outperforming existing baselines. LMDX PaLM 2-S demonstrates data efficiency by achieving similar extraction quality at zero-shot as baselines with 10-100 training documents. It also generalizes well to unseen templates compared to baselines. The performance on hierarchical entities, such as line items, is significantly better than baselines.
Ablation studies are conducted to assess the importance of different facets of the LMDX methodology. Removing the base entity extraction training stage results in a significant drop in extraction quality, especially in few-shot scenarios and zero-shot extraction. Removing coordinate tokens, which communicate spatial information to the LLM, leads to a substantial decrease in extraction quality. Discarding the sampling strategy slightly reduces extraction quality but also removes the error correction capability.
Error analysis reveals that a common error pattern in LMDX is caused by OCR lines grouping multiple semantically different segments, leading to incorrect associations in predictions. Incorporating the image modality is identified as a potential solution to address this limitation.
In conclusion, LMDX is a highly effective methodology for extracting information from visually rich documents using LLMs. It achieves state-of-the-art results, demonstrates data efficiency, and enables accurate extraction of singular, repeated, and hierarchical entities. Future work includes incorporating the image modality and exploring open-source LLMs for further improvements.
This document is a summary of the paper "Language Model-Based Document Information Extraction and Localization" by Rohan Anil, et al. The paper discusses the use of language models for extracting and localizing information from documents. The authors propose a method called Language Model-based Document eXtraction (LMDX) that combines the power of language models with traditional document analysis techniques.
The LMDX framework consists of three main components: document chunking, entity value parsing, and responses decoding. Document chunking involves dividing the document into smaller segments based on token limits. The authors present a chunking algorithm that prunes segments from the end of the page to fit within the token limit. Entity value parsing focuses on extracting values for specific entities from the document. The authors propose an algorithm that parses entity values using regular expressions and segment mappings. Responses decoding involves decoding the extracted entities from multiple language model responses using majority voting.
The authors evaluate the LMDX framework on three benchmark datasets: VRDU Ad-Buy Form, VRDU Registration Form, and CORD. They compare the performance of LMDX with two baselines: LayoutLMv3 LARGE and Donut. LayoutLMv3 LARGE is a model that combines text and layout information for document understanding, while Donut is a model specifically designed for table extraction.
The results show that LMDX outperforms both baselines on all three datasets. LMDX achieves higher accuracy in extracting entities and localizing information compared to LayoutLMv3 LARGE and Donut. The authors attribute the success of LMDX to the use of language models, which have the ability to learn complex patterns and relationships between entities in documents.
The authors also conduct ablation studies to analyze the impact of different components of LMDX. They find that both document chunking and entity value parsing contribute significantly to the overall performance of LMDX. Additionally, they observe that using line index tags instead of coordinate grounding tags does not significantly affect the performance of LMDX, suggesting that the model can interpret the layout of the document without explicit coordinate information.
In conclusion, the paper presents LMDX as an effective framework for document information extraction and localization. The results demonstrate the superiority of LMDX over existing baselines in terms of accuracy and performance. The authors suggest that language model-based approaches have great potential for improving document analysis and understanding tasks.