LLM Structured Output Benchmarks
The LLM Structured Output Benchmarks repository provides a comprehensive framework for benchmarking various LLM (Large Language Model) structured output frameworks. This includes popular frameworks such as Instructor, Mirascope, Langchain, LlamaIndex, Fructose, Marvin, and Outlines. The benchmarks focus on key tasks including:
- Multi-label Classification: Predicting multiple labels associated with a given text.
- Named Entity Recognition (NER): Extracting entities from text.
- Synthetic Data Generation: Creating synthetic data based on specified schemas.
Key Features:
- Diverse Frameworks: Supports multiple LLM frameworks for a wide range of applications.
- Detailed Metrics: Provides reliability, latency, precision, recall, and F1 score metrics for thorough evaluation.
- Contribution Friendly: Open for contributions, allowing users to add new frameworks and benchmarks easily.
Benefits:
- Comprehensive Evaluation: Users can evaluate the performance of different frameworks on standardized tasks.
- Community Driven: Encourages collaboration and feedback from users to improve the benchmarks.
- Open Source: Freely available for anyone to use, modify, and contribute to.
Highlights:
- Easy setup and execution of benchmarks using Python scripts.
- Clear guidelines for adding new frameworks and tasks.
- Regular updates and community engagement to enhance the repository's capabilities.
