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User seeks LLM advice for accurate PDF to JSON data mapping

A user is seeking advice on improving the accuracy of mapping data from PDF documents into a JSON format using local large language models. After using Docling to parse PDFs into markdown, the user employs a Qwen 3.5-9B model to convert this markdown into a specific JSON structure. However, the LLM struggles with accurately mapping data, leading to incorrect labels for counts and amounts, suggesting a potential limitation in the model's capabilities rather than the workflow itself. The user is looking for solutions that can generalize across different files and is constrained to local models due to API limitations. AI

IMPACT This query highlights common challenges in applying LLMs for structured data extraction and the need for improved model capabilities or fine-tuning techniques.

RANK_REASON User is asking for advice on a technical problem related to LLM usage, not reporting a new release or significant event.

Read on r/LocalLLaMA →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

User seeks LLM advice for accurate PDF to JSON data mapping

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  1. r/LocalLLaMA TIER_1 English(EN) · /u/CatSweaty4883 ·

    Pdf to JSON, 3 months in.

    <!-- SC_OFF --><div class="md"><p>Hello all, it has been 3 months since I made the initial post, where I wanted ideas to try out. The subreddit has been amazing with responses, and the most success I had was using pymupdf4llm or Docling. I have been sticking to docling for how ac…