Synthetic Data for Robust LLM Application Evaluation

Name: Synthetic%20Data%20for%20Robust%20LLM%20Application%20Evaluation
Uploaded: 2023-10-24
Description: We%20will%20start%20with%20a%20brief%20overview%20of%20synthetic%20data%20generation.%20Next,%20we%20will%20inspect%20its%20effectiveness%20by%20referring%20to%20some%20of%20the%20latest%20works%20in%20this%20space.%20Then%20we%20will%20move%20on%20to%20see%20how%20this%20can%20be%20leveraged%20in%20evaluating%20LLM-driven%20applications%20pipelines.

Posted Oct 24, 2023 | Views 660

# Synthetic Data

# Application Evaluation

# ExplodingGradients

speakers

Shahul Es

Founder @ Exploding Gradients

I am Data science expert well acquainted with Data analytics, classical ML, NLP, and audio processing. I am a Kaggle GrandMaster. I love contributing to opensource and has contributed & created several opensource ML projects.

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Adam Becker

IRL @ MLOps Community

I'm a tech entrepreneur and I spent the last decade founding companies that drive societal change.

I am now building Deep Matter, a startup still in stealth mode...

I was most recently building Telepath, the world's most developer-friendly machine learning platform. Throughout my previous projects, I had learned that building machine learning powered applications is hard - especially hard when you don't have a background in data science. I believe that this is choking innovation, especially in industries that can't support large data teams.

For example, I previously co-founded Call Time AI, where we used Artificial Intelligence to assemble and study the largest database of political contributions. The company powered progressive campaigns from school board to the Presidency. As of October, 2020, we helped Democrats raise tens of millions of dollars. In April of 2021, we sold Call Time to Political Data Inc.. Our success, in large part, is due to our ability to productionize machine learning.

I believe that knowledge is unbounded, and that everything that is not forbidden by laws of nature is achievable, given the right knowledge. This holds immense promise for the future of intelligence and therefore for the future of well-being. I believe that the process of mining knowledge should be done honestly and responsibly, and that wielding it should be done with care. I co-founded Telepath to give more tools to more people to access more knowledge.

I'm fascinated by the relationship between technology, science and history. I graduated from UC Berkeley with degrees in Astrophysics and Classics and have published several papers on those topics. I was previously a researcher at the Getty Villa where I wrote about Ancient Greek math and at the Weizmann Institute, where I researched supernovae.

I currently live in New York City. I enjoy advising startups, thinking about how they can make for an excellent vehicle for addressing the Israeli-Palestinian conflict, and hearing from random folks who stumble on my LinkedIn profile. Reach out, friend!

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SUMMARY

We will start with a brief overview of synthetic data generation. Next, we will inspect its effectiveness by referring to some of the latest works in this space. Then we will move on to see how this can be leveraged in evaluating LLM-driven applications pipelines.

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