The dominant search methods today typically rely on keyword matching or vector space similarity to estimate the relevance between a query and documents. However, these techniques have difficulties when it comes to searching corpora using files, articles, or even entire books as search queries.
Keyword-based recovery
While keyword searches are great for short searches, they fail to capture the semantics critical for long-form content. A document that correctly discusses “cloud platforms” may go unnoticed in a query seeking “AWS” expertise. Exact term matches frequently face problems of vocabulary mismatch in long texts.
Vector Similarity Search
Modern vector embedding models like BERT condensed meaning into hundreds of numerical dimensions by accurately estimating semantic similarity. However, self-attention transformer architectures do not exceed 512-1024 tokens due to compute explosion.
Without the ability to fully assimilate documents, the resulting partial embeddings of “bags of words” lose the nuances of meaning interspersed across sections. Context is lost in abstraction.
The prohibitive computational complexity also restricts the fine-tuning of most real-world corpora, limiting accuracy. Unsupervised learning offers an alternative, but robust techniques are lacking.
in a recent article, researchers address exactly these problems by reimagining the relevance of very long queries and documents. Their innovations unlock new potential for ai document search.
Today’s dominant search paradigms are ineffective for queries that contain thousands of words as input text. Key issues faced include:
- Transformers like BERT they have quadratic self-attention complexity, which makes them infeasible for sequences beyond 512-1024 tokens. Its few care alternatives compromise precision.
- Lexical models Matching based on exact term overlaps cannot infer critical semantic similarities for long-form texts.
- The lack of labeled training data for most domain collections requires…