Leaderboards for Evaluating Language Models

Leaderboards test a models' ability to perform across diverse tasks, including factual accuracy, general knowledge, reasoning, and ethical alignment.

YouTube Benchmarks explainer - bycloud

They incorporate benchmarks like MMLU (Massive Multitask Language Understanding measures general academic and professional knowledge), TruthfulQA  (tests the truthfulness of responses), and HellaSwag (testing commonsense reasoning and natural language inference) to test different aspects of model performance.

* Stanford Holistic Evaluation of Language Models (HELM) Leaderboard - A reproducible and transparent framework for evaluating foundation models. These leaderboards cover many scenarios, metrics, and models with support for multimodality and model-graded evaluation.

* Artificial Analysis provides benchmarking and related information to support people & organizations in choosing the right model for their use-case and which provider to use for that model.

* Chatbot Arena is an open-source research project developed by members from LMSYS and UC Berkeley SkyLab. It has built an open crowdsourced platform to collect human feedback and evaluate 30+ LLMs under real-world scenarios. You can compare two anonymous models at a time.

* The HuggingFace leaderboard is based on the following three benchmarks -

  • Chatbot Arena - a crowdsourced, randomized battle platform. We use 100K+ user votes to compute Elo ratings.
  • MT-Bench - a set of challenging multi-turn questions. We use GPT-4 to grade the model responses.
  • MMLU (5-shot) - a test to measure a model's multitask accuracy on 57 tasks.

Related - How to choose the right LLM

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