{"id":34119,"date":"2023-12-08T03:00:00","date_gmt":"2023-12-08T11:00:00","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=34119"},"modified":"2023-12-07T15:33:53","modified_gmt":"2023-12-07T23:33:53","slug":"hallucination-index-identifies-best-llms-for-most-popular-ai-use-cases","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2023\/12\/08\/hallucination-index-identifies-best-llms-for-most-popular-ai-use-cases\/","title":{"rendered":"Hallucination Index Identifies Best LLMs for Most Popular AI Use Cases"},"content":{"rendered":"
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Galileo<\/a>, a leading machine learning (ML) company for unstructured data, released a Hallucination Index developed by its research arm, Galileo Labs, to help users of today\u2019s leading LLMs determine which model is least likely to hallucinate for their intended application. The findings can be viewed HERE<\/a>. <\/p>\n\n\n\n

\u201c2023 has been the year of LLMs. While everyone from individual developers to Fortune 50 enterprises has been learning how to wrangle this novel new technology, two things are clear: first, LLMs are not one size fits all and second, hallucinations remain one of the greatest hurdles to LLM adoption,\u201d said Atindriyo Sanyal, Galileo\u2019s co-founder and CTO. \u201cTo help builders identify which LLMs to use for their applications, Galileo Labs created a ranking of the most popular LLMs based on their propensity to hallucinate using our proprietary hallucination evaluation metrics, Correctness and Context Adherence. We hope this effort sheds light on LLMs and helps teams pick the perfect LLM for their use case.\u201d<\/em><\/p>\n\n\n\n

While businesses of all sizes are building LLM-based applications, these efforts are being hindered by hallucinations that pose significant challenges in generating accurate and reliable responses. With hallucinations, AI generates information that appears realistic at first glance yet is ultimately incorrect or disconnected from the context. <\/p>\n\n\n\n

To help teams get a handle on hallucinations and identify the best LLM that suits their needs, Galileo Labs developed a Hallucination Index that takes 11 LLMs from Open AI (GPT-4-0613, GPT-3.5-turbo-1106, GPT-3.5-turbo-0613 and GPT-3.5-turbo-instruct), Meta (Llama-2-70b, Llama-2-13b and Llama-2-7b), TII UAE (Falcon-40b-instruct), Mosaic ML (MPT-7b-instruct), Mistral.ai (Mistral-7b-instruct) and Hugging Face (Zephyr-7b-beta) and evaluates each LLM\u2019s likelihood to hallucinate in common generative AI task types.<\/p>\n\n\n\n

Key insights include:<\/p>\n\n\n\n