Summary of An Encoding–searching Separation Perspective on Bi-encoder Neural Search, by Hung-nghiep Tran et al.
An Encoding–Searching Separation Perspective on Bi-Encoder Neural Search
by Hung-Nghiep Tran, Akiko Aizawa, Atsuhiro Takasu
First submitted to arxiv on: 2 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper revisits the bi-encoder architecture for neural search, which is widely used due to its simplicity and scalability. Despite its popularity, this architecture has some notable issues, including low performance on seen datasets and weak zero-shot performance on new datasets. The authors analyze these problems and identify two main critiques: the encoding information bottleneck problem and limitations of the basic assumption of embedding search. They propose a thought experiment to logically analyze the encoding and searching operations and challenge the basic assumption of embedding search. This leads to the development of a new perspective, called the encoding–searching separation perspective, which conceptually and practically separates the encoding and searching operations. The authors apply this new perspective to explain the root cause of the identified issues and discuss ways to mitigate them. Finally, they discuss the implications of this new perspective, the design surface it exposes, and potential research directions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at a popular way to search for things using computers called neural search. The current method is simple and works well but has some big problems. It doesn’t do very well on data it’s seen before and does even worse on new data. The authors try to figure out why this is happening and come up with two main ideas: that the way information is encoded is a problem, and that the assumptions behind how searching works are not quite right. They propose a thought experiment to test these ideas and challenge the current assumptions. This leads to a new way of thinking about neural search called the encoding–searching separation perspective. The authors use this new perspective to explain why things aren’t working well and suggest ways to fix them. |
Keywords
* Artificial intelligence * Embedding * Encoder * Zero shot
