30/07/2024
🚀 NEWS from AI world
Galileo, a leading developer of generative AI for enterprise applications, has released its latest Hallucination Index.
🔍 Key Takeaways:
- 22 prominent LLMs* evaluated: Including models from big names like OpenAI, Anthropic, Google, and Meta.
- The index includes 11 new models, reflecting the rapid growth in both open- and closed-source LLMs over the past eight months.
- Focus on real-world applications: Using Galileo’s context adherence metric to check for inaccuracies in outputs across various input lengths (1,000 to 100,000 tokens).
💡 Findings & Trends:
1️⃣ Top Performer: Anthropic’s Claude 3.5 Sonnet scored near-perfect across short, medium, and long context scenarios.
2️⃣ Cost-Effective Champion: Google’s Gemini 1.5 Flash excelled in cost-effectiveness, delivering strong performance across all tasks.
3️⃣ Top Open-Source Model: Alibaba’s Qwen2-72B-Instruct shined, particularly in short and medium context scenarios.
4️⃣ Rapid Advancement: Open-source models are closing the gap with their closed-source counterparts, improving hallucination performance at lower costs.
5️⃣ Context Handling: Current RAG LLMs* are better at managing extended context lengths without quality compromises.
6️⃣ Efficiency Over Scale: Smaller models sometimes outperformed larger ones, showing that efficient design can be more crucial than sheer scale.
As the AI industry continues to evolve and tackle the challenges posed by hallucinations, Galileo’s Hallucination Index offers crucial insights for making informed decisions about which model fits your needs and budget.
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*LLMs - (Large Language Models) are a type of artificial intelligence (AI) that are trained on massive amounts of text data. This allows them to understand, interpret, and generate human-like text.
*RAG LLMs - (Retrieval Augmented Generation Large Language Models) - instead of relying solely on the data they were trained on, RAG LLMs can search and retrieve relevant information from an external knowledge base in real time to enhance their responses.
