OpenAI Embeddings (and Controversy?!)

OpenAI Embeddings (and Controversy?!)

Feb 16, 2022
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#mlnews #openai #embeddings COMMENTS DIRECTLY FROM THE AUTHOR (thanks a lot for reaching out Arvind :) ): 1. The FIQA results you share also have code to reproduce the results in the paper using the API: https://twitter.com/arvind_io/status/1488257004783112192?s=20&t=gB3c79VEX8hGJl6WfZa2iA There's no discrepancy AFAIK. 2. We leave out 6 not 7 BEIR datasets. Results on msmarco, nq and triviaqa are in a separate table (Table 5 in the paper). NQ is part of BEIR too and we didn't want to repeat it. Finally, the 6 datasets we leave out are not readily available and it is common to leave them out in prior work too. For examples, see SPLADE v2 (https://arxiv.org/pdf/2109.10086.pdf) also evaluates on the same 12 BEIR datasets. 3. Finally, I'm now working on time travel so that I can cite papers from the future :) END COMMENTS FROM THE AUTHOR OpenAI launches an embeddings endpoint in their API, providing high-dimensional vector embeddings for use in text similarity, text search, and code search. While embeddings are universally recognized as a standard tool to process natural language, people have raised doubts about the quality of OpenAI's embeddings, as one blog post found they are often outperformed by open-source models, which are much smaller and with which embedding would cost a fraction of what OpenAI charges. In this video, we examine the claims made and determine what it all means. OUTLINE: 0:00 - Intro 0:30 - Sponsor: Weights & Biases 2:20 - What embeddings are available? 3:55 - OpenAI shows promising results 5:25 - How good are the results really? 6:55 - Criticism: Open models might be cheaper and smaller 10:05 - Discrepancies in the results 11:00 - The author's response 11:50 - Putting things into perspective 13:35 - What about real world data? 14:40 - OpenAI's pricing strategy: Why so expensive? Sponsor: Weights & Biases https://wandb.me/yannic Merch: store.ykilcher.com ERRATA: At 13:20 I say "better", it should be "worse" References: https://openai.com/blog/introducing-text-and-code-embeddings/ https://arxiv.org/pdf/2201.10005.pdf https://beta.openai.com/docs/guides/embeddings/what-are-embeddings https://beta.openai.com/docs/api-reference/fine-tunes https://twitter.com/Nils_Reimers/status/1487014195568775173?s=20&t=NBF7D2DYi41346cGM-PQjQ https://medium.com/@nils_reimers/openai-gpt-3-text-embeddings-really-a-new-state-of-the-art-in-dense-text-embeddings-6571fe3ec9d9 https://mobile.twitter.com/arvind_io/status/1487188996774002688 https://twitter.com/gwern/status/1487096484545847299 https://twitter.com/gwern/status/1487156204979855366 https://twitter.com/Nils_Reimers/status/1487216073409716224 https://twitter.com/gwern/status/1470203876209012736 https://www.reddit.com/r/MachineLearning/comments/sew5rl/d_it_seems_openais_new_embedding_models_perform/ https://mobile.twitter.com/arvind_io/status/1488257004783112192 https://mobile.twitter.com/arvind_io/status/1488569644726177796 Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://discord.gg/4H8xxDF BitChute: https://www.bitchute.com/channel/yannic-kilcher LinkedIn: https://www.linkedin.com/in/ykilcher BiliBili: https://space.bilibili.com/2017636191 If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannickilcher Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

0:00 - Intro 0:30 - Sponsor: Weights & Biases 2:20 - What embeddings are available? 3:55 - OpenAI shows promising results 5:25 - How good are the results really? 6:55 - Criticism: Open models might be cheaper and smaller 10:05 - Discrepancies in the results 11:00 - The author's response 11:50 - Putting things into perspective 13:35 - What about real world data? 14:40 - OpenAI's pricing strategy: Why so expensive?
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