The Role of Synthetic Data in Reducing Human Workload

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Synthetic data is about reducing the human effort needed to create a useful dataset, rather than automating everything. It involves curating a dataset that a model generates, selecting preferred examples, and editing a few of them. This approach is particularly beneficial for studying millions of small properties or developing unit tests for language models, as it's impractical for humans to create them from scratch. The use of weak labeling in synthetic data generation has been a topic of discussion, with some experts believing it might have been too early to adopt and still holding faith in its potential within deep learning.

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In lieu of an extended preamble, we encourage you to listen and click through all the interviews and show notes, all of which have been curated to match the references mentioned in the episode.

Timestamps & Show Notes

* [00:01:26] Jonathan Frankle - Chief Scientist, MosaicML/Databricks

* see also the Mosaic/MPT-7B episode

* $1.3B MosaicML x Databricks acquisition

* [00:22:11] Lin Qiao - CEO, Fireworks AI

* Fireworks Mixtral

* [00:38:24] Aman Sanger - CEO, Anysphere (Cursor)

* see also the Cursor episode

* $8m seed from OpenAI

* Tweet: Request-level memory-based KV caching

* Tweet: GPT-4 grading and Trueskill ratings for rerankers

* [00:51:14] Aravind Srinivas - CEO, Perplexity