Description-Based Text Similarity
arxiv(2023)
摘要
Identifying texts with a given semantics is central for many information
seeking scenarios. Similarity search over vector embeddings appear to be
central to this ability, yet the similarity reflected in current text
embeddings is corpus-driven, and is inconsistent and sub-optimal for many use
cases. What, then, is a good notion of similarity for effective retrieval of
text?
We identify the need to search for texts based on abstract descriptions of
their content, and the corresponding notion of description based
similarity. We demonstrate the inadequacy of current text embeddings and
propose an alternative model that significantly improves when used in standard
nearest neighbor search. The model is trained using positive and negative pairs
sourced through prompting a LLM, demonstrating how data from LLMs can be used
for creating new capabilities not immediately possible using the original
model.
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