Are LLMs good enough for Document Extraction? (www.unsiloed.ai)

Recent discussions have surfaced about the effectiveness of large language models (LLMs) in document extraction tasks, such as reading receipts or invoices from scanned PDFs or photos. While LLMs like Claude Opus 4.8 demonstrate capabilities in handling clean, one-off documents with straightforward questions, their performance declines significantly with complex or degraded inputs. For example, in extracting data from dense documents or handwritten text, LLMs may produce plausible but incorrect information without clearly indicating errors. This discrepancy highlights the limitations of relying solely on LLMs for high-stakes tasks, where consistency and accuracy are paramount. The significance of this revelation for the AI/ML community lies in the continued need for specialized document extraction models, such as Unsiloed, which can provide additional layers of reliability and checks. These dedicated models return extracted values with confidence scores and citations, enabling users to assess the trustworthiness of the output and enhance data accuracy through checks and balances. As workflows increasingly involve large volumes of documents, utilizing both LLMs for reasoning and dedicated models for accurate reading becomes imperative. Ultimately, the best approach combines the strengths of LLMs and specialized models to optimize efficiency, accuracy, and cost in document processing environments.