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🔥 New LLM leaderboard on the hub: an LLM Hallucination Leaderboard!
🔥 New LLM leaderboard on the hub: an LLM Hallucination Leaderboard!
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Led by 
Led by 
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@pminervini
pminervini
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 , it evaluates the propensity of models to *hallucinate*, either on factuality (= say false things) or faithfulness (= ignore user instructions). This is becoming an increasingly important avenue of research, as more and more people are starting to rely on LLMs to find and search for information! 
 , it evaluates the propensity of models to *hallucinate*, either on factuality (= say false things) or faithfulness (= ignore user instructions). This is becoming an increasingly important avenue of research, as more and more people are starting to rely on LLMs to find and search for information! 
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It contains 14 datasets, grouped over 7 concepts, to try to get a better overall view of when LLMs output wrong content.
It contains 14 datasets, grouped over 7 concepts, to try to get a better overall view of when LLMs output wrong content.
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resource
https://huggingface.co/spaces/hallucinations-leaderboard/leaderboard?logs=container
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hallucinations-leaderboard/leaderboard
https://huggingface.co/spaces/hallucinations-leaderboard/leaderboard?logs=container
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Their introductory blog post also contains an in depth analysis of which LLMs get what wrong, which is super interesting: 
Their introductory blog post also contains an in depth analysis of which LLMs get what wrong, which is super interesting: 
link
https://huggingface.co/blog/leaderboards-on-the-hub-hallucinations
https://huggingface.co/blog/leaderboards-on-the-hub-hallucinations
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Congrats to the team! 🚀
Congrats to the team! 🚀
🔥 New LLM leaderboard on the hub: an LLM Hallucination Leaderboard!

Led by @pminervini , it evaluates the propensity of models to *hallucinate*, either on factuality (= say false things) or faithfulness (= ignore user instructions). This is becoming an increasingly important avenue of research, as more and more people are starting to rely on LLMs to find and search for information! 
It contains 14 datasets, grouped over 7 concepts, to try to get a better overall view of when LLMs output wrong content.
https://huggingface.co/spaces/hallucinations-leaderboard/leaderboard?logs=container

Their introductory blog post also contains an in depth analysis of which LLMs get what wrong, which is super interesting: https://huggingface.co/blog/leaderboards-on-the-hub-hallucinations

Congrats to the team! 🚀
❤️
clem gsarti kgourgou Rami
👍
mvaloatto gsarti kgourgou