#022 The Limits of Embeddings, Out-of-Domain Data, Long Context, Finetuning (and How We're Fixing It)
How AI Is Built
Train for Your Domain to Succeed
Text embedding models exhibit significant limitations when applied outside their training domains, leading to poor performance on unfamiliar data. These models excel in tasks similar to their training sets but struggle in diverse environments like community forums, news articles, or scientific papers, often reverting to lexical search methods that are more effective in these contexts. Researchers stress the importance of out-of-domain performance over in-domain training results, as most users operate without access to specific training data. This performance gap is particularly evident when dealing with niche or lesser-known subjects, which the model may not recognize, complicating the retrieval of accurate information. Overall, a critical understanding of the training context of embedding models is crucial for effectively utilizing them in varied applications.
