LMDX: Language Model-based Document Information Extraction and Localization
Topics: AI (Deep Learning), Data Mining, Document Classification, Entity based search, Indexing, LLMO, Ranking, Semantic Search
The paper titled LMDX: Language Model-based Document Information Extraction and Localization from Google Deepmind introduces a methodology for using large language models (LLMs) to extract and localize entities from visually rich documents (VRD). Traditional methods struggle with document layouts and hierarchical entities, and they often rely on significant human annotation. LMDX overcomes these challenges by reframing the extraction task for LLMs, enabling both entity localization and extraction in a data-efficient way. It introduces layout encoding and a decoding algorithm that helps discard hallucinations from the LLMs, leading to improved accuracy.
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