In-Short
- Galileo releases Hallucination Index evaluating 22 Gen AI LLMs including models from OpenAI, Anthropic, Google, and Meta.
- Anthropic’s Claude 3.5 Sonnet tops overall performance; Google’s Gemini 1.5 Flash excels in cost-effectiveness.
- Open-source models are improving, challenging closed-source counterparts, and smaller models show efficiency.
Summary of the Hallucination Index Findings
Galileo, a pioneer in generative AI for businesses, has unveiled its latest Hallucination Index, a comprehensive evaluation of 22 leading Generative AI Large Language Models (LLMs). This year’s index has grown to include 11 new models, reflecting the swift expansion of both open- and closed-source LLMs.
The index, which emphasizes Retrieval Augmented Generation (RAG), uses Galileo’s proprietary metric, context adherence, to measure output accuracy across different input lengths. This helps enterprises weigh the trade-offs between cost and performance when implementing AI solutions.
Among the key findings, Anthropic’s Claude 3.5 Sonnet was recognized for its near-perfect scores in various context scenarios, while Google’s Gemini 1.5 Flash was noted for its cost-effective performance. Alibaba’s Qwen2-72B-Instruct was highlighted as the leading open-source model, especially in shorter contexts.
The report also identified several trends, such as the narrowing performance gap between open- and closed-source models, the improved handling of extended contexts by current RAG LLMs, and the occasional superiority of smaller models over larger ones. Additionally, the emergence of strong models from outside the US, like Mistral’s Mistral-large and Alibaba’s qwen2-72b-instruct, points to increasing global competition in LLM development.
While closed-source models like Claude 3.5 Sonnet and Gemini 1.5 Flash maintain a lead due to proprietary training data, the index shows a rapidly evolving landscape, with Google’s open-source Gemma-7b model underperforming compared to its closed-source counterpart.
Galileo’s Hallucination Index offers critical insights for businesses aiming to select the most suitable AI model for their needs and budget, addressing the challenge of hallucinations in production-ready Gen AI products.
For more detailed insights, visit the original article on AI News.
