Modeling and Automating Human Preferences for LLM Evaluation
As large language models (LLMs) continue to revolutionize industries, companies face the challenge of evaluating these models' outputs efficiently and consistently. Traditional human evaluation processes can be highly valuable but are time-consuming, inconsistent, and difficult to scale. This webinar introduces a cutting-edge technique to automate the LLM evaluation process by learning the preferences of your human raters.
Join Kolena, a leader in AI evaluation and quality standards, as we explore:
- The current landscape of LLM evaluation and its limitations
- How to leverage human evaluation data to fine-tune an LLM
- Techniques for modeling human preferences and decision-making processes
- Accelerating model development by bootstrapping human evaluations
- Implementing automated evaluation systems that align with human judgment
Learn how this innovative approach can significantly increase product quality without spiking evaluation costs. Whether you're a data scientist, AI engineer, or business leader, this webinar will provide valuable insights into how to achieve the best possible quality for your LLM.
Register now to learn how you can harness the power of AI to streamline a human preference evaluation process.
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Meet the speakers
Join our expert speakers for a presentation and Q&A
Gordon Hart
After being burned one too many times by unexpected model performance in mission-critical production scenarios, Gordon co-founded Kolena to fix fundamental problems with ML testing practices across the industry.
Prior to Kolena, Gordon designed, implemented, and deployed computer vision products for defense and security as Head of Product at Synapse (acq. by Palantir) and at Palantir.
Skip Everling
At Kolena, Skip's role as Head of Developer Relations. His objective is to help ML/AI engineers and data scientists more effectively test and refine their models so that they perform robustly in the real world.