In this webinar, Arthur's ML Engineers Max Cembalest & Rowan Cheung shared best practices and learnings from using LLMs to evaluate other LLMs.
They covered:
• Evolving Evaluation: LLMs require new evaluation methods to determine which models are best suited for which purposes.
• LLMs as Evaluators: LLMs are used to assess other LLMs, leveraging their human-like responses and contextual understanding.
• Biases and Risks: Understanding biases in LLM responses when judging other models is essential to ensure fair evaluations.
• Relevance and Context: LLMs can create testing datasets that better reflect real-world context, enhancing model applicability assessment.
More links you might find useful:
• Learn more about Arthur Bench, our LLM evaluation product → [ Ссылка ]
• Check out the Arthur Bench GitHub → [ Ссылка ]
• Join us on Discord → [ Ссылка ]
——
About Arthur:
Arthur is the AI performance company. Our platform monitors, measures, and improves machine learning models to deliver better results. We help data scientists, product owners, and business leaders accelerate model operations and optimize for accuracy, explainability, and fairness.
Arthur’s research-led approach to product development drives exclusive capabilities in LLMs, computer vision, NLP, bias mitigation, and other critical areas. We’re on a mission to make AI work for everyone, and we are deeply passionate about building ML technology to drive responsible business results.
Learn more about Arthur → [ Ссылка ]
Follow us on Twitter → [ Ссылка ]
Follow us on LinkedIn → [ Ссылка ]
Sign up for our newsletter → [ Ссылка ]
