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Model Refinement

Several open-source projects and initiatives focus on developing standards and benchmarks for testing and evaluating language models (LLMs) and their performance on various tasks, including prompt-based evaluations.

EleutherAI Language Model Evaluation Harness

  • Developed by EleutherAI, an open-source research community
  • Includes a wide range of benchmarks and evaluation tasks
  • Aims to provide a standardized way to assess LLM performance across different dimensions, such as language understanding, generation, and robustness
  • GitHub: https://github.com/EleutherAI/lm-evaluation-harness

BIG-bench (Beyond the Imitation Game Benchmark)

  • Open-source, collaborative benchmark designed to probe large language models and push the boundaries of their capabilities
  • Includes a collection of tasks and benchmarks to assess various aspects of language model performance, including creativity, logical reasoning, and adaptability
  • GitHub: https://github.com/google/BIG-bench

GLUE (General Language Understanding Evaluation) Benchmark

  • Collection of resources for evaluating and analyzing the performance of models across a diverse range of natural language understanding tasks
  • Not specifically designed for prompt-based evaluation but provides a standardized set of tasks and metrics for assessing LLMs
  • Website: https://gluebenchmark.com/

SuperGLUE Benchmark [#rag]

  • An updated and more challenging version of the GLUE benchmark
  • Designed to push the limits of language understanding and reasoning capabilities of AI systems
  • Includes a set of more difficult language understanding tasks that require complex reasoning and knowledge transfer
  • Website: https://super.gluebenchmark.com/

Hugging Face Evaluate

  • Open-source library provided by Hugging Face, a popular platform for natural language processing (NLP) models and tools
  • Includes a collection of evaluation modules and metrics for assessing NLP models, including LLMs
  • Not exclusively focused on prompt-based evaluation but offers a range of tools and resources for standardized model testing
  • GitHub: https://github.com/huggingface/evaluate

These projects and initiatives contribute to the development of standardized methods and benchmarks for evaluating LLMs and their performance on various tasks, enabling researchers and developers to compare and assess different models, identify areas for improvement, and push the boundaries of LLM capabilities.