Podcast: Shaping the Accuracy of LLM-Generated Content with Outlines

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, we are joined by Dan Gerlanc and Remi Louf from the new startup .txt to explore guided outputs for Large Language Models (LLMs) and their tool, Outlines.

Although the requirements of casual users of LLMs are modest (clear responses, understandable answers, or engaging dialogue) and are achievable without special guidance, those of more advanced users are more stringent and require guided outputs.

For example, if you use an LLM through an Application Programming Interface (API) without proper guidance, the LLM might return information in a format that’s incompatible or requires significant reformatting, making it less efficient or even unusable for the intended purpose.

Over the course of this episode, Remi and Dan take you through the basics of guided output generation, including what it is, when it should be used, and what steps can be taken to achieve it. They also take you through some of the commonly faced challenges of guided outputs and possible solutions.

You’ll also learn about Outlines, and how it can address some of the difficulties to creating guided outputs, and potentially make it less computationally intensive to do so.

Start listening now to explore the world of guided outputs and the role of Outlines in that world with Remi and Dan, 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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