The second annual Index, which ranks the top
22 leading language models lists Anthropic's Claude 3.5 Sonnet as
the best performing model across all tasks
SAN
FRANCISCO, July 29, 2024 /PRNewswire/
-- Galileo, a leader in developing generative AI for the
enterprise, today announced the launch of its latest Hallucination
Index, a Retrieval Augmented Generation (RAG)-focused evaluation
framework, which ranks the performance of 22 leading Generative AI
(Gen AI) large language models (LLMs) from brands like OpenAI,
Anthropic, Google, and Meta.
This year's Index added 11 models to the framework, representing
the rapid growth in both open- and closed-source LLMs in just the
past 8 months. As brands race to create bigger, faster, and more
accurate models, hallucinations remain the main hurdle to deploying
production-ready Gen AI products.
Which LLM Performed the Best
The Index tests open-and
closed-sourced models using Galileo's proprietary evaluation
metric, context adherence, designed to check for output
inaccuracies and help enterprises make informed decisions about
balancing price and performance. Models were tested with inputs
ranging from 1,000 to 100,000 tokens, to understand performance
across short (less than 5k tokens),
medium (5k to 25k tokens), and long context (40k to 100k tokens)
lengths.
- Best Overall Performing Model: Anthropic's Claude 3.5
Sonnet. The closed-source model outpaced competing models across
short, medium, and long context scenarios. Anthropic's Claude 3.5
Sonnet and Claude 3 Opus consistently scored close to perfect
scores across categories, beating out last year's winners, GPT-4o
and GPT-3.5, especially in shorter context scenarios.
- Best Performing Model on Cost: Google's Gemini 1.5
Flash. The Google model ranked the best performing for the cost due
to its great performance on all tasks.
- Best Open Source Model: Alibaba's Qwen2-72B-Instruct. The open source model performed
best with top scores in the short and medium context.
"In today's rapidly evolving AI landscape, developers and
enterprises face a critical challenge: how to harness the power of
generative AI while balancing cost, accuracy, and reliability.
Current benchmarks are often based on academic use-cases, rather
than real-world applications. Our new Index seeks to address this
by testing models in real-world use cases that require the LLMs to
retrieve data, a common practice in enterprise AI implementations,"
says Vikram Chatterji, CEO and
Co-founder of Galileo. "As hallucinations continue to be a major
hurdle, our goal wasn't to just rank models, but rather give AI
teams and leaders the real-world data they need to adopt the right
model, for the right task, at the right price."
Key Findings and Trends:
- Open-Source Closing the Gap: Closed-source models like
Claude-3.5 Sonnet and Gemini 1.5 Flash remain the top performers
thanks to proprietary training data, but open-source models, such
as Qwen1.5-32B-Chat and
Llama-3-70b-chat, are rapidly closing
the gap with improvements in hallucination performance and
lower-cost barriers than their closed-source counterparts.
- Overall Improvements with Long Context Lengths: Current
RAG LLMs, like Claude 3.5 Sonnet, Claude-3-opus and Gemini 1.5 pro
001 perform particularly well with extended context lengths —
without losing quality or accuracy — reflecting the progress being
made with both model training and architecture.
- Large Models Are Not Always Better: In certain cases,
smaller models outperform larger models. For example,
Gemini-1.5-flash-001 outperformed larger models, which suggests
that efficiency in model design can sometimes outweigh scale.
- From National to Global Focus: LLMs from outside of the
U.S. such as Mistral's Mistral-large and Alibaba's
qwen2-72b-instruct are emerging
players in the space and continue to grow in popularity,
representing the global push to create effective language
models.
- Room for Improvement: While Google's open-source
Gemma-7b performed the worst, their closed-source Gemini 1.5 Flash
model consistently landed near the top.
See a complete breakdown of Galileo's Hallucination Index
results here.
About Galileo's Context Adherence Evaluation Model
Context Adherence uses a proprietary method created by Galileo
Labs called ChainPoll to measure how well an AI model adheres to
the information it is given, helping spot when AI makes up
information that is not in the original text.
About Galileo
San Francisco-based Galileo is
the leading platform for enterprise GenAI evaluation and
observability. The Galileo platform, powered by Luna™
Evaluation Foundation Models (EFMs), supports AI teams across the
development lifecycle, from building and iterating to monitoring
and protection. Galileo is used by AI teams from startups to
Fortune 100 companies. Visit rungalileo.io to learn more about the
Galileo suite of products.
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SOURCE Galileo