Andrew Yates (Assistant Professor at the University of Amsterdam) and Sergi Castella i Sapé discuss the recent "Open Pre-trained Transformer (OPT) Language Models" from Meta AI (formerly Facebook).
In this replication work, Meta replicated OpenAI's GPT-3 training as per their original paper, documenting the process in detail, including the nitty gritty details, to share their findings with the community. The code, pretrained weights, and logbook are available on their Github repository (links below).
📄 OPT paper: https://arxiv.org/abs/2205.01068
✍️ OPT Official Blog Post: https://ai.facebook.com/blog/democratizing-access-to-large-scale-language-models-with-opt-175b/
OpenAI Embeddings API: https://openai.com/blog/introducing-text-and-code-embeddings/
Nils Reimers' critique of OpenAI Embeddings API: https://medium.com/@nils_reimers/openai-gpt-3-text-embeddings-really-a-new-state-of-the-art-in-dense-text-embeddings-6571fe3ec9d9
00:00 Introduction and housekeeping: new feedback form, ACL conference highlights
02:42 The convergence between NLP and Neural IR techniques
06:43 Open Pretrained Transformer motivation and scope, reproducing GPT-3 and open-sourcing
08:16 Basics of OPT: architecture, pre-training objective, teacher forcing, tokenizer, training data
13:40 Preliminary experiments findings: hyperparameters, training stability, spikiness
20:08 Problems that appear at scale when training with 992 GPUs
23:01 Using temperature to check whether GPUs are working
25:00 Training the largest model: what to do when the loss explodes? (which happens quite often)
29:15 When they switched away from AdamW to SGD
32:00 Results: successful but not quite GPT-3 level. Toxicity?
35:45 Replicability of Large Language Models research. Was GPT-3 replicable? What difference does it make?
37:25 What makes a paper replicable?
40:33 Directions in which large Language Models are applied to Information Retrieval
45:15 Final thoughts and takeaways