Text embeddings have limitations when it comes to handling long documents and out-of-domain data.
Today, we are talking to Nils Reimers. He is one of the researchers who kickstarted the field of dense embeddings, developed sentence transformers, started HuggingFace’s Neural Search team and now leads the development of search foundational models at Cohere. Tbh, he has too many accolades to count off here.
We talk about the main limitations of embeddings:
- Failing out of domain
- Struggling with long documents
- Very hard to debug
- Hard to find formalize what actually is similar
Are you still not sure whether to listen? Here are some teasers:
- Interpreting embeddings can be challenging, and current models are not easily explainable.
- Fine-tuning is necessary to adapt embeddings to specific domains, but it requires careful consideration of the data and objectives.
- Re-ranking is an effective approach to handle long documents and incorporate additional factors like recency and trustworthiness.
- The future of embeddings lies in addressing scalability issues and exploring new research directions.
Nils Reimers:
Nicolay Gerold:
text embeddings, limitations, long documents, interpretation, fine-tuning, re-ranking, future research
00:00 Introduction and Guest Introduction
00:43 Early Work with BERT and Argument Mining
02:24 Evolution and Innovations in Embeddings
03:39 Constructive Learning and Hard Negatives
05:17 Training and Fine-Tuning Embedding Models
12:48 Challenges and Limitations of Embeddings
18:16 Adapting Embeddings to New Domains
22:41 Handling Long Documents and Re-Ranking
31:08 Combining Embeddings with Traditional ML
45:16 Conclusion and Upcoming Episodes
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