Hugging Face’s updated leaderboard shakes up the AI evaluation game

by | Jun 26, 2024 | Technology

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In a move that could reshape the landscape of open-source AI development, Hugging Face has unveiled a significant upgrade to its Open LLM Leaderboard. This revamp comes at a critical juncture in AI development, as researchers and companies grapple with an apparent plateau in performance gains for large language models (LLMs).

The Open LLM Leaderboard, a benchmark tool that has become a touchstone for measuring progress in AI language models, has been retooled to provide more rigorous and nuanced evaluations. This update arrives as the AI community has observed a slowdown in breakthrough improvements, despite the continuous release of new models.

Pumped to announce the brand new open LLM leaderboard. We burned 300 H100 to re-run new evaluations like MMLU-pro for all major open LLMs!Some learning:– Qwen 72B is the king and Chinese open models are dominating overall– Previous evaluations have become too easy for recent…— clem ? (@ClementDelangue) June 26, 2024

Addressing the plateau: A multi-pronged approach

The leaderboard’s refresh introduces more complex evaluation metrics and provides detailed analyses to help users understand which tests are most relevant for specific applications. This move reflects a growing awareness in the AI community that raw performance numbers alone are insufficient for assessing a model’s real-world utility.

Key changes to the leaderboard include:

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Introduction of more challenging datasets that test advanced reasoning and real-world knowledge application.

Implementation of multi-turn dialogue evaluations to assess models’ conversational abilities more thoroughly.

Expansion of non-English language evaluations to better represent global AI capabilities.

Incorporation of tests for instruction-following and few-shot learning, which are increasingly importa …

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Don’t miss OpenAI, Chevron, Nvidia, Kaiser Permanente, and Capital One leaders only at VentureBeat Transform 2024. Gain essential insights about GenAI and expand your network at this exclusive three day event. Learn More

In a move that could reshape the landscape of open-source AI development, Hugging Face has unveiled a significant upgrade to its Open LLM Leaderboard. This revamp comes at a critical juncture in AI development, as researchers and companies grapple with an apparent plateau in performance gains for large language models (LLMs).

The Open LLM Leaderboard, a benchmark tool that has become a touchstone for measuring progress in AI language models, has been retooled to provide more rigorous and nuanced evaluations. This update arrives as the AI community has observed a slowdown in breakthrough improvements, despite the continuous release of new models.

Pumped to announce the brand new open LLM leaderboard. We burned 300 H100 to re-run new evaluations like MMLU-pro for all major open LLMs!Some learning:– Qwen 72B is the king and Chinese open models are dominating overall– Previous evaluations have become too easy for recent…— clem ? (@ClementDelangue) June 26, 2024

Addressing the plateau: A multi-pronged approach

The leaderboard’s refresh introduces more complex evaluation metrics and provides detailed analyses to help users understand which tests are most relevant for specific applications. This move reflects a growing awareness in the AI community that raw performance numbers alone are insufficient for assessing a model’s real-world utility.

Key changes to the leaderboard include:

Countdown to VB Transform 2024

Join enterprise leaders in San Francisco from July 9 to 11 for our flagship AI event. Connect with peers, explore the opportunities and challenges of Generative AI, and learn how to integrate AI applications into your industry. Register Now

Introduction of more challenging datasets that test advanced reasoning and real-world knowledge application.

Implementation of multi-turn dialogue evaluations to assess models’ conversational abilities more thoroughly.

Expansion of non-English language evaluations to better represent global AI capabilities.

Incorporation of tests for instruction-following and few-shot learning, which are increasingly importa …nnDiscussion:nn” ai_name=”RocketNews AI: ” start_sentence=”Can I tell you more about this article?” text_input_placeholder=”Type ‘Yes'”]

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