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Galileo's Luna®: Revolutionizing AI Evaluation

General Report November 10, 2024
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TABLE OF CONTENTS

  1. Summary
  2. Introduction to Galileo’s Luna EFMs
  3. Performance Metrics
  4. Challenges in Traditional AI Evaluations
  5. Addressing Hallucinations in AI Models
  6. Galileo’s Annual Hallucination Index
  7. Application of Luna® EFMs Across Industries
  8. Conclusion

1. Summary

  • Galileo introduces Luna®, a pioneering Evaluation Foundation Model (EFM) set to revolutionize enterprise AI evaluations by enhancing speed, accuracy, and cost-effectiveness. This innovative model addresses significant challenges in traditional AI evaluations, such as costly and slow human assessments and inefficient large language model methods like OpenAI's GPT-3.5. Luna® EFMs demonstrate remarkable abilities to evaluate AI responses 11 times faster and 97% cheaper while achieving up to 20% higher accuracy. These advancements are crucial for real-time, large-scale AI evaluations, establishing Luna® as an attractive solution for enterprises. The report delves into Luna® EFMs' application across industries, focusing on finance and banking, which benefit from its capabilities in mitigating risks such as data security and systemic decision-making hurdles. Additionally, Galileo's Annual Hallucination Index provides insights into the effectiveness of AI models across various performance metrics, underscoring Luna®'s potential in addressing AI hallucinations and improving reliability in enterprise applications.

2. Introduction to Galileo’s Luna EFMs

  • 2-1. Overview of Luna® EFMs

  • Galileo has introduced a pioneering suite of Evaluation Foundation Models (EFMs) called Luna®. These models aim to revolutionize the evaluation of generative AI solutions by providing high accuracy, low latency, and minimal cost. Luna® EFMs have been developed to address significant challenges related to the deployment of generative AI in enterprises, offering faster, more cost-effective evaluations than traditional methods such as askGPT and human evaluations.

  • 2-2. Development and Purpose of Luna®

  • The development of Luna® was driven by feedback from customers who found existing evaluation methods, like human 'vibe checks' and LLM-based evaluations, to be costly and slow. With this context, Galileo set out to create EFMs that establish new benchmarks in speed, accuracy, and cost efficiency, thereby facilitating the deployment of trustworthy AI solutions in production environments.

  • 2-3. Key Features and Innovations

  • Luna® EFMs significantly outperform traditional evaluation methods in several key metrics: 1. **Speed**: Luna® EFMs evaluate AI responses 11 times faster than methods like GPT-3.5 and human evaluations. 2. **Cost-Efficiency**: Evaluating with Luna® models is 97% cheaper compared to using OpenAI’s GPT-3.5. 3. **Accuracy**: Luna® EFMs are 18% more accurate than OpenAI GPT-3.5 and up to 20% more accurate than Galileo’s Chainpoll LLM. These features make Luna® EFMs a highly attractive option for enterprises seeking real-time, large-scale AI evaluations.

3. Performance Metrics

  • 3-1. Speed of Evaluations

  • Galileo's Luna® EFMs can evaluate AI responses 11 times faster than traditional evaluation methods, such as GPT-3.5 and human evaluations. This enhancement significantly addresses the evaluation latency challenges faced by enterprises, allowing for near real-time evaluations of generative AI outputs.

  • 3-2. Cost Efficiency

  • The Luna® EFMs are 97% cheaper than using OpenAI's GPT-3.5 for evaluations. This dramatic reduction in costs makes large-scale AI evaluations more accessible and feasible for enterprises, eliminating financial barriers related to traditional evaluation methods.

  • 3-3. Accuracy Improvements

  • The accuracy of Luna® EFMs is reported to be 18% higher than that of OpenAI GPT-3.5 and up to 20% more accurate than Galileo's own Chainpoll large language model (LLM). These improvements are critical for enterprises needing to assess AI responses for issues such as hallucinations, toxicity, and security risks, thereby enhancing the reliability of generative AI applications.

4. Challenges in Traditional AI Evaluations

  • 4-1. Limitations of Human Evaluations

  • Traditional human evaluations for AI models are often prohibitively expensive and slow. This approach, frequently referred to as 'vibe checks', lacks scalability for enterprise-level applications. According to reports, due to their high cost, human evaluations are not feasible for large-scale AI assessments, which highlights a critical limitation in the evaluation framework.

  • 4-2. Challenges with LLM-based Methods

  • Large language model (LLM)-based evaluation methods, like those utilizing OpenAI's GPT-3.5, also present significant challenges. These methods tend to be both cost-prohibitive and sluggish, resulting in evaluation processes that do not meet the demands of enterprise applications. Specifically, traditional LLM evaluations are reported to be 97% more expensive than Galileo's Luna EFMs and 11 times slower, emphasizing their inefficiency in comparison.

  • 4-3. Cost and Time Inefficiencies

  • The existing methods of AI evaluations are characterized by considerable cost and time inefficiencies. Human evaluations and LLM-based assessments are too slow and costly, creating barriers to the effective deployment of AI systems at scale. Reports indicate that typical costs associated with traditional evaluation methods significantly hinder enterprises’ ability to conduct timely and cost-effective assessments of AI outputs.

5. Addressing Hallucinations in AI Models

  • 5-1. Understanding AI Hallucinations

  • AI hallucinations refer to the phenomenon where artificial intelligence models generate erroneous or misleading outputs despite appearing confident. This challenge significantly undermines the reliability and trust of AI applications. Traditional evaluation methods, including human assessments and large language model-based evaluations, struggle to effectively identify and address these inaccuracies. A comprehensive understanding of hallucinations is crucial for ensuring the integrity of AI-generated content.

  • 5-2. Luna® EFMs in Detecting Hallucinations

  • Galileo's Luna® Evaluation Foundation Models (EFMs) have proven to be significantly more effective in detecting AI hallucinations compared to traditional methods. Specifically, they demonstrate an 18% higher accuracy in identifying hallucinations compared to OpenAI's GPT-3.5 evaluations. This improved accuracy is essential for enterprises that rely on AI for generating content, as it enhances the reliability of the outcomes produced by these models.

  • 5-3. Impact of Hallucinations on Enterprise Trust

  • The presence of hallucinations in AI outputs poses substantial risks to enterprise trust and reliability. As organizations increasingly deploy generative AI solutions, the challenge of addressing inaccuracies and ensuring content integrity becomes paramount. Galileo's advancements in evaluation methodologies, particularly through the implementation of Luna® EFMs, aim to enhance enterprise trust by providing robust mechanisms to detect and mitigate hallucinations effectively.

6. Galileo’s Annual Hallucination Index

  • 6-1. Introduction to the Hallucination Index

  • Galileo recently released its second annual Hallucination Index, which evaluates the performance of 22 leading Generative AI (Gen AI) large language models (LLMs) from various brands, including OpenAI, Anthropic, Google, and Meta. The Index applies a Retrieval Augmented Generation (RAG)-focused evaluation framework along with Galileo’s proprietary Context Adherence evaluation model. This year's Index has expanded by adding 11 new models, reflecting the rapid growth and diversification in both open- and closed-source LLMs over just the past eight months, addressing the challenges presented by AI hallucinations.

  • 6-2. Performance Evaluation of Leading LLMs

  • The Hallucination Index evaluates LLMs based on their adherence to given contexts to detect when models produce inaccurate outputs, known as hallucinations. Input text sizes ranged from 1,000 to 100,000 tokens to assess performance across short, medium, and long contexts. Key findings include: 1. **Best Overall Performing Model**: Anthropic’s Claude 3.5 Sonnet excelled across all context lengths, outperforming competitors like OpenAI’s GPT-4o. 2. **Best Performing Model on Cost**: Google’s Gemini 1.5 Flash demonstrated excellent performance concerning cost across multiple tasks. 3. **Best Open Source Model**: Alibaba’s Qwen2-72B-Instruct achieved top performance in short and medium context tasks. The Index also revealed that smaller models could outperform larger models, indicating that efficiency sometimes surpasses scale.

  • 6-3. Key Findings and Trends

  • The Hallucination Index addresses criticisms of traditional benchmarks by evaluating models in realistic, enterprise-focused scenarios, which are critical for enterprise AI applications. Important trends from the Index include: 1. **Open-Source Models Closing the Gap**: While closed-source models like Claude 3.5 Sonnet and Gemini 1.5 Flash lead, open-source models such as Qwen1.5--Chat and Llama-3--chat are rapidly improving in performance and cost-effectiveness. 2. **Improved Performance with Longer Contexts**: Certain models, like Claude 3.5 Sonnet, have shown considerable enhancements in managing longer context lengths without sacrificing quality. 3. **Global Expansion**: Models from outside the U.S., such as Mistral’s Mistral-large, are gaining significance, highlighting a global push for more effective language models. The proprietary Context Adherence evaluation model ensures that models are evaluated against real-world standards, providing enterprises with practical insights to select the most suitable models for their needs.

7. Application of Luna® EFMs Across Industries

  • 7-1. Finance and Banking Applications

  • Galileo's Luna Evaluation Foundation Models (EFMs) are demonstrating transformative capabilities in the finance and banking sectors. According to insights from PYMNTS Intelligence, Generative AI (GenAI) is enhancing customer interactions and improving risk models within this industry. However, the implementation of GenAI also brings about challenges related to data security and systemic decision-making risks. These challenges have prompted regulators to take action, particularly regarding model explainability. In a statement, Alex Klug from HP noted that traditional evaluation methods, including human evaluations and other large language models (LLMs), have been costly and slow. Luna is intended to address these significant hurdles by providing faster, more accurate, and cost-effective evaluation processes.

  • 7-2. Regulatory and Security Considerations

  • The introduction of Luna EFMs comes amid growing regulatory scrutiny in the financial services sector. The use of GenAI in finance raises important considerations regarding data security and the reliability of decision-making processes. Authorities are increasingly focused on the implications of model explainability, making it essential for businesses to adopt evaluation frameworks that align with regulatory standards. The challenges presented by existing models have been noted, as they struggle to ensure both accuracy and security. The result is an increased urgency for solutions that can offer trustworthy AI applications in compliance with regulations.

  • 7-3. Industry-Specific Solutions

  • Luna EFMs are tailored to offer industry-specific solutions by focusing on the unique requirements of various sectors. For instance, in RAG (retrieval-augmented generation) environments, Luna models have been specifically fine-tuned to detect and mitigate hallucinations, which are situations where AI generates information that isn't supported by the retrieved context. This capability is critical for industries relying heavily on accurate data responses, such as finance and healthcare. Furthermore, Luna has demonstrated excellent performance on the RAGTruth dataset and shows significant improvements over previous models like GPT-3.5 and RAGAS, achieving a 97% reduction in cost and a 96% reduction in latency compared to these models. This positions Luna as a highly efficient and effective solution across various industry applications.

Conclusion

  • The introduction of Galileo's Luna® Evaluation Foundation Models marks a transformative step in enterprise AI evaluations by addressing critical issues like hallucinations, cost, and speed. Luna® EFMs stand out with superior performance metrics, offering enterprises effective and practical solutions to deploy reliable AI applications at scale. The Annual Hallucination Index supports this by highlighting key advancements in AI evaluation methodologies, further emphasizing the importance of robust frameworks in enhancing AI reliability. Despite these advancements, the report acknowledges potential limitations, such as the need for ongoing model updates and adaptation to evolving regulatory frameworks. Future prospects for Luna® EFMs point towards wider applicability across industries, particularly those heavily reliant on AI, like finance and healthcare, where data precision is paramount. Practical applications of this model are vast, and with continued innovation, Luna® EFMs are poised to meet the demands of regulatory standards and provide safer AI applications, thus bolstering enterprise trust and efficiency in the long term.

Glossary

  • Galileo Luna® [Evaluation Foundation Model]: Galileo Luna® is a suite of Evaluation Foundation Models designed to enhance the evaluation process of generative AI solutions. These models provide high accuracy, low latency, and cost-efficient evaluations, addressing traditional challenges in AI assessments. Luna® EFMs are instrumental in identifying hallucinations and ensuring the reliability of AI-generated content, making them crucial for enterprises looking to deploy trustworthy AI applications at scale.

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