Summary of Comparing Neighbors Together Makes It Easy: Jointly Comparing Multiple Candidates For Efficient and Effective Retrieval, by Jonghyun Song et al.
Comparing Neighbors Together Makes it Easy: Jointly Comparing Multiple Candidates for Efficient and Effective Retrieval
by Jonghyun Song, Cheyon Jin, Wenlong Zhao, Andrew McCallum, Jay-Yoon Lee
First submitted to arxiv on: 21 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Information Retrieval (cs.IR); Machine Learning (cs.LG)
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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 A novel framework called Comparing Multiple Candidates (CMC) is proposed to overcome the limitations of retrieve-and-rerank paradigms. CMC leverages shallow self-attention layers to compare query embeddings with multiple candidate embeddings, generating rich representations contextualized to each other. This approach enables scalable comparisons of thousands of candidates while maintaining performance comparable to cross-encoders. Experimental results on ZeSHEL demonstrate that incorporating CMC as an intermediate reranker improves recall@k by 4.8%-p and 3.5%-p for R@16 and R@64, respectively, with negligible slowdown. Additionally, CMC is shown to be faster (11x) and often more effective than cross-encoders in downstream tasks such as entity linking (+0.7%-p) and dialogue ranking (+3.3%-p). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Comparing Multiple Candidates (CMC) is a new way of comparing lots of things that are similar to each other. This helps make sure the results are more accurate. It works by looking at how similar different things are, rather than just comparing them one by one. This makes it much faster and better than previous methods. In tests, CMC was able to find the right answer more often and quicker than before. |
Keywords
» Artificial intelligence » Entity linking » Recall » Self attention
