For all the transformative and disruptive power attributed to the rise of artificial intelligence, the Achilles’ heel of generative AI remains its tendency to make things up.

The tendency of Large Language Models (LLMs) to “hallucinate” comes with all sorts of pitfalls, sowing the seeds of misinformation. The sphere of Natural Language Processing (NLP) can be dangerous, especially when people cannot tell the difference between what is human and what is AI generated.

To get a handle on the situation, Huggingface—which claims to be the world’s largest Open Source AI community—introduced the Hallucinations Leaderboard, a new ranking dedicated to evaluating open source LLMs and their tendency to generate hallucinated content by running them through a set of different benchmarks tailored for in-context learning.

“This initiative wants to aid researchers and engineers in identifying the most reliable models, and potentially drive the development of LLMs towards more accurate and faithful language generation,” the leaderboard developers explained.

The spectrum of hallucinations in LLMs breaks down into two distinct categories: factuality and

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Author: Jose Antonio Lanz

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