Popular LLM leaderboards and additional sources for more information

---

😎 Find some popular LLM leaderboards[huggingface.co][nextbigfuture.com][huggingface.co]

🤖 I found some popular LLM leaderboards that might interest you:

1. **The Big Benchmarks Collection** on Hugging Face collects benchmark spaces beyond the Open LLM Leaderboard, aiming to track, rank, and evaluate open LLMs and chatbots. This platform allows for the submission of models for automated evaluation on a GPU cluster [ref].

2. **LLM Leaderboard Best Models** also on Hugging Face, offers a daily updated list of models with the best evaluations on the LLM leaderboard. It covers models across different sizes, including best pretrained models around specific parameters such as 1B, 3B, 7B, and upwards, addressing various aspects like text generation and domain-specific fine-tuning [ref].

3. **NextBigFuture** discusses the Open LLM Leaderboard, which evaluates LLMs and chatbots against 4 key benchmarks: AI2 Reasoning Challenge, HellaSwag, MMLU, and TruthfulQA. These benchmarks are selected for their ability to test a variety of reasoning and general knowledge across a wide field in both 0-shot and few-shot settings. It highlights the leading open-source models like llama-65b and MetaIX/GPT4-X-Alpasta-30b [ref].

Each of these resources provides a unique perspective on evaluating and comparing large language models, offering insights into their capabilities and performances across different benchmarks and tasks.[huggingface.co][nextbigfuture.com][huggingface.co]

😎 can you find more?[toloka.ai][vellum.ai]

🤖 I found additional LLM leaderboards with detailed benchmarks and model comparisons that could be of interest:

1. **Vellum AI’s LLM Leaderboard for 2024** provides a comprehensive comparison of several models like GPT-4, Claude 3, Gemini Pro, and others across various benchmarks including MMLU, GSM-8K, and HumanEval. This leaderboard focuses on largest context windows, lowest input costs per 1M tokens, and performance across different tasks such as multi-choice questions, reasoning, and Python coding. Notable findings include Claude 3 Opus having a significant lead in tasks like HumanEval (0 shot) and GSM-8K [ref].

2. **Toloka’s LLM Leaderboard** emphasizes practical application, comparing models like WizardLM, LLaMA 2, Vicuna, GPT-4, and GPT-3.5 Turbo across different fields such as brainstorming and Q&A, using unique organic prompts for evaluation. Their process involves two stages: prompt collection from real interactions with ChatGPT to ensure relevance and a human evaluation phase where experts assess model outputs. This approach provides insights into how these models perform on real-world tasks, offering a unique angle compared to more traditional benchmarking methods [ref].

These resources offer valuable insights for anyone looking to understand the capabilities and performance of different large language models, catering to both technical benchmarks and practical application scenarios.[toloka.ai][vellum.ai]

---

Hugging Face Dataset Metrics

All the conversation prompts, responses, and metrics are available to download and explore on Hugging Face dataset reddgr/talking-to-chatbots-chats: