Guiding the Narrative: Shaping the Accuracy of LLM-Generated Content with Outlines with Remi Louf and Dan Gerlanc ODSC's Ai X Podcast
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- Technology
In this episode of ODSC’s Ai X Podcast, you’ll explore guided outputs for Large Language Models. Casual users of LLMs expect the responses they receive to be clear, understandable answers or engaging dialogue, which is achievable without special guidance. If you want to use an LLM through an Application Programming Interface (API), you will need to guide the outputs. 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. To discuss the role and use of guided output, Remi Louf and Dan Gerlanc from a new startup, .txt, have joined the podcast.
Sponsored by: https://odsc.com/
Find more ODSC lightning interviews, webinars, live trainings, certifications, bootcamps here – https://aiplus.training/
QUESTION
* Dan, Remi. Tell us about your background?
* "Why is it not always feasible to train LLMs to produce more deterministic and systematic outputs.
* What is guided output generation?
* "What steps can be taken to standardize the outputs of LLMs for use in systems that require uniform and systematic data?"
* "What inspired the creation of Outlines, and what specific challenges in working with LLMs were you aiming to address?"
* "Why is structuring both the input and output of LLMs considered of primary importance for their effective deployment?"
* Deterministic systems require consistent and predictable outputs. How can we ensure LLMs don't generate significantly different outputs for the same input under similar conditions? Does outlines help with that?
* "Could you provide a brief technical overview of how Outlines works to guide the outputs of LLMs?"
* “Why do you believe that the issue of standardization and structuring outputs/inputs will not be solved by LLM providers or existing tools like LangChain?"
* "What are the unique features of Outlines that differentiate it from other tools or frameworks in this space?"
* "What are some practical applications or use cases where Outlines significantly improves the output of LLMs?"
* LLMs can "hallucinate," generating seemingly factual but ultimately untrue information. How can we be confident that LLM outputs used in deterministic systems won't introduce harmful inaccuracies?
* What kinds of LLMs can you use outlines with?
* "As we scale to multiple agents and LLMs, what are the anticipated complexities in standardizing outputs and inputs, and how does Outlines address these?"
* Using LLMs is a significant computational and cost burden. Can Outlines help optimize workflows to lower cost.
* "What are the future plans or upcoming features for Outlines? Is there a roadmap for the project's evolution?"
* Tell us about your startup.
Show Notes:
Guided Text Generation Paper: https://arxiv.org/abs/2307.09702
Outlines Github project: https://github.com/outlines-dev/outlines
In this episode of ODSC’s Ai X Podcast, you’ll explore guided outputs for Large Language Models. Casual users of LLMs expect the responses they receive to be clear, understandable answers or engaging dialogue, which is achievable without special guidance. If you want to use an LLM through an Application Programming Interface (API), you will need to guide the outputs. 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. To discuss the role and use of guided output, Remi Louf and Dan Gerlanc from a new startup, .txt, have joined the podcast.
Sponsored by: https://odsc.com/
Find more ODSC lightning interviews, webinars, live trainings, certifications, bootcamps here – https://aiplus.training/
QUESTION
* Dan, Remi. Tell us about your background?
* "Why is it not always feasible to train LLMs to produce more deterministic and systematic outputs.
* What is guided output generation?
* "What steps can be taken to standardize the outputs of LLMs for use in systems that require uniform and systematic data?"
* "What inspired the creation of Outlines, and what specific challenges in working with LLMs were you aiming to address?"
* "Why is structuring both the input and output of LLMs considered of primary importance for their effective deployment?"
* Deterministic systems require consistent and predictable outputs. How can we ensure LLMs don't generate significantly different outputs for the same input under similar conditions? Does outlines help with that?
* "Could you provide a brief technical overview of how Outlines works to guide the outputs of LLMs?"
* “Why do you believe that the issue of standardization and structuring outputs/inputs will not be solved by LLM providers or existing tools like LangChain?"
* "What are the unique features of Outlines that differentiate it from other tools or frameworks in this space?"
* "What are some practical applications or use cases where Outlines significantly improves the output of LLMs?"
* LLMs can "hallucinate," generating seemingly factual but ultimately untrue information. How can we be confident that LLM outputs used in deterministic systems won't introduce harmful inaccuracies?
* What kinds of LLMs can you use outlines with?
* "As we scale to multiple agents and LLMs, what are the anticipated complexities in standardizing outputs and inputs, and how does Outlines address these?"
* Using LLMs is a significant computational and cost burden. Can Outlines help optimize workflows to lower cost.
* "What are the future plans or upcoming features for Outlines? Is there a roadmap for the project's evolution?"
* Tell us about your startup.
Show Notes:
Guided Text Generation Paper: https://arxiv.org/abs/2307.09702
Outlines Github project: https://github.com/outlines-dev/outlines