.txt

.txt is a company focused on developing and promoting structured generation for large language models (LLMs). They believe that structured generation, which allows LLMs to output text adhering to specific formats like regular expressions, JSON schemas, or context-free grammars, is essential for building reliable and scalable software systems around LLMs. .txt offers the Outlines library, an open-source framework for implementing structured generation, and is actively researching ways to improve the performance and flexibility of this technology.

.txt's research has uncovered several key benefits of structured generation. Their work demonstrates that structured generation can significantly improve LLM performance on tasks like function calling, even exceeding the performance of proprietary models from OpenAI. Additionally, they have discovered that structured generation can achieve high performance with fewer examples, leading to prompt efficiency, and even make LLM inference significantly faster through a technique called "coalescence."

.txt is a company focused on revolutionizing the way we interact with Large Language Models (LLMs) by developing and promoting structured generation technology. Their mission is to empower developers and researchers with tools that enable them to control the output of LLMs, ensuring predictable and reliable results, while also improving performance and efficiency. They believe that structured generation is not only essential for building robust and scalable software systems around LLMs, but also has the potential to unlock new levels of accuracy and speed in various tasks involving LLMs.

The company promotes a culture of innovation and open collaboration, evident in its open-source contributions and commitment to sharing research findings through blog posts. The focus on structured generation reflects a desire for rigorous engineering principles in the field of LLMs, emphasizing a shift towards reliable and scalable AI software systems.