Troubleshooting Large Language Models with Amber Roberts
Learn about cutting-edge developments in AI and Data Science from the experts who know them best on ODSC’s Ai X Podcast. Each week we release an interview with a leading expert, core contributor, experienced practitioner, or acclaimed instructor who is helping to shape the future of the AI industry through their work or research.
In this episode, Amber Roberts of Arize AI brings her years of experience working with models as a data scientist and machine learning engineer to one of the pivotal developments of 2023 and 2024, Large Language Models (LLMS). Join her for an exploration of that essential topic, troubleshooting.
Every data scientist will run into issues and problems that they will have to troubleshoot. But, what is the best way to choose the correct metrics, fix errors, and deal with hallucinations? During this discussion, Amber dives into the reasons why troubleshooting Large Language Models is so difficult, specific issues that she’s encountered in her work, and strategies for resolving them with a focus on the 9 parts for LLMs.
Amber also takes time to do a deeper dive into hallucinations in particular, with a discussion on what constitutes a hallucination, what causes them, and the techniques and tools you can use to identify unobvious hallucinations.
You’ll hear about Phoenix, an early-stage interpretability tool for LLMs, and learn how it helps with the evaluation, troubleshooting, and fine-tuning of LLMs.
And last, but not least, Amber will leave you with some advice on how to make the most of your AI career in this rapidly changing industry.
Start listening now to take full advantage of Amber’s expertise and knowledge, and don’t forget to subscribe to ODSC’s Ai X Podcast to ensure you never miss an episode. Finally, like what you hear? Leave a review or share it with a friend! You can listen on Spotify, Apple, and SoundCloud.
Originally posted on OpenDataScience.com
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