Show HN: Make a free 3.8B model as reliable as one 7× bigger at parsing data (pypi.org)

A new open-source project, **LLM Feedback Control**, has been introduced that enhances the reliability of smaller language models (LLMs) by employing a deterministic feedback mechanism. By integrating a regime gate, exact graph analysis, and explicit refusal responses, this framework allows a 3.8 billion parameter model to produce structured outputs as dependable as that from a 26 billion parameter model. This significant advancement addresses the common issue of LLMs generating confident but inaccurate results when extracting structured data from text. The core innovation lies in the feedback loop, which cross-verifies the model's output against a deterministic reference, fills in gaps through follow-up queries, and outright refuses to answer when uncertain. This approach effectively extracts various types of structured information, such as workflows and form fields, while sidestepping the problems associated with open-ended generation, where there’s no reference for validation. The tool’s zero runtime dependencies and straightforward installation make it accessible for users, supporting diverse applications and offering a glimpse into how smaller models can become valuable in practical AI applications without extensive computational resources.