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Google's open-source tool 'langextract' uses LLMs to extract structured data from unstructured text with grounded character positions, crossing 37k GitHub stars.
This paper explores distilling a large reasoning teacher model (8B) into a small student model (0.6B) for on-device structured text enrichment, achieving substantial speedup while recovering significant quality. The study finds that the reasoning nature of the teacher, rather than its scale, drives improvement in summary quality, but a same-size instruction teacher yields more faithful outputs on certain articles.
This paper presents an automated pipeline that uses the DeepSeek V3 model to decompose Italian tax-court judgments into individual legal issues structured in XML following the IRAC framework, and includes a hallucination-detection filter using the Linkoln parser to validate citations, validated by expert annotators.
Datalab releases lift, a model that extracts structured JSON from PDFs and images using schema-constrained decoding, with local and hosted inference options.
A developer shares lessons from building a local document-to-JSON extractor using llama3.2 3B on Ollama, highlighting that deterministic post-processing and schema-constrained outputs matter more than model size, while seeking feedback on hallucination and context truncation issues with long documents.
The author upgraded their Hermes agents with TencentDB Agent Memory, using a local Qwen 3.5-4B model via llama-server for structured JSON extraction and multi-step tool use, implementing a resilient layered memory pipeline with cursor-based checkpointing.
Numind released NuExtract3, a 4B open-weight vision-language model based on Qwen3.5-4B, designed for converting document images to Markdown, OCR, and structured data extraction. It is Apache-2.0 licensed and self-hostable with quantized versions for low VRAM.
NuExtract3 is a 4B vision-language reasoning model for document understanding, enabling structured extraction and image-to-Markdown conversion.