Summary of Distillation Enhanced Generative Retrieval, by Yongqi Li et al.
Distillation Enhanced Generative Retrieval
by Yongqi Li, Zhen Zhang, Wenjie Wang, Liqiang Nie, Wenjie Li, Tat-Seng Chua
First submitted to arxiv on: 16 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 presents a new approach to text retrieval called generative retrieval, which generates identifier strings of relevant passages as the retrieval target. The authors leverage powerful language models, distinct from traditional sparse or dense retrieval methods. They propose a framework called DGR that utilizes sophisticated ranking models in a teacher role to supply a passage rank list and optimizes the generative retrieval model using a distilled RankNet loss. This framework requires only an additional distillation step and does not add any burden to the inference stage. The authors conduct experiments on four public datasets, achieving state-of-the-art performance among generative retrieval methods and demonstrating exceptional robustness and generalizability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding the most relevant text passages in a big library. It’s like searching for a specific book, but instead of giving you exact words to look for, it gives you some hints to find related texts. The authors used special language models that can understand what’s important and what’s not. They created a system called DGR that makes the search even better by using another model as a teacher to help decide which passages are most relevant. This way, DGR only needs one extra step to make it work, and it doesn’t slow down the searching process. The authors tested DGR on lots of different texts and showed that it’s really good at finding what you’re looking for. |
Keywords
» Artificial intelligence » Distillation » Inference
