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Is there standardised benchmarking being done to compare the various capabilities of LLMs objectively?

Generation Details

Date LLM Access Method
Nov 2024 GPT-4 Web UI

For periodic assessments of large language models (LLMs) across various parameters, there are several websites and publications you can follow. These sources provide open-source results from standardized testing:

Websites and Publications

  1. Vellum AI - LLM Leaderboard: This site offers a comprehensive comparison of LLM capabilities, including coding benchmarks, cost, and context length. It provides detailed performance metrics for leading models[3].
  2. HELM (Holistic Evaluation of Language Models): HELM evaluates LLMs on a variety of metrics, including accuracy, calibration, robustness, fairness, bias, toxicity, and efficiency. It is known for its comprehensive approach to model evaluation[5].
  3. BIG-Bench: A benchmark that includes a wide range of tasks such as text generation, question answering, and commonsense reasoning. It is used to evaluate the general capabilities of LLMs[5].
  4. DeepEval: An open-source evaluation framework that offers a variety of metrics like summarization, hallucination, faithfulness, and contextual relevancy. It is designed to provide detailed insights into LLM performance[2].
  5. Analytics Vidhya: Offers insights into various benchmarks and evaluations for LLMs, focusing on both functional and non-functional criteria[4].

Key Benchmarks

  • GLUE and SuperGLUE: Focus on natural language understanding tasks.
  • MMLU (Massive Multitask Language Understanding): Tests models across a wide range of subjects to assess their breadth and depth of knowledge[6].
  • HumanEval: Specifically targets coding capabilities by evaluating code generation tasks.

These sources and benchmarks will help you stay informed about the latest developments in LLM performance across various tasks.

The above text was generated by a large language model (LLM) and its accuracy has not been validated. This page is part of 'LLMs-on-LLMs,' a Github repository by Daniel Rosehill which explores how curious humans can use LLMs to better their understanding of LLMs and AI. However, the information should not be regarded as authoritative and given the fast pace of evolution in LLM technology will eventually become deprecated. This footer was added at 16-Nov-2024.