How AI Is Built
#022 The Limits of Embeddings, Out-of-Domain Data, Long Context, Finetuning (and How We're Fixing It)
Sep 19, 2024
Join Nils Reimers, a prominent researcher in dense embeddings and the driving force behind foundational search models at Cohere. He dives into the intriguing limitations of text embeddings, such as their struggles with long documents and out-of-domain data. Reimers shares insights on the necessity of fine-tuning to adapt models effectively. He also discusses innovative approaches like re-ranking to enhance search relevance, and the bright future of embeddings as new research avenues are explored. Don't miss this deep dive into the cutting-edge of AI!
46:06
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Quick takeaways
- Text embeddings struggle with out-of-domain data and long documents, making fine-tuning essential for enhanced effectiveness in specific contexts.
- The evolution of embeddings highlights the significance of leveraging existing models and employing two-stage retrieval processes for improved accuracy.
Deep dives
The Evolution of Embeddings
The discussion elaborates on the development of embeddings since the introduction of BERT, highlighting its application in argument mining and text clustering. Initially, pairwise classification was used to assess argument similarities, but scalability issues led to the exploration of embeddings for more efficient clustering. This evolution has enabled more effective semantic text processing and retrieval, indicating a significant shift towards unifying whether similar texts can be used for clustering and finding relevant answers based on a user's query. As the field progressed, better models emerged, including InferSent and innovations like contrastive learning, which improved the way embeddings are generated and assessed.
