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New framework enhances generative retrieval by using discarded quantization data

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new framework called Residual Trajectory Distillation (ResTD) to improve generative retrieval systems. This method aims to leverage information from the residual quantization process, which is typically discarded during standard retrieval training. By distilling this discarded information, ResTD enhances the retrieval process and has shown consistent improvements in multilingual e-commerce retrieval tasks. The framework is also adaptable for generative recommendation systems. AI

IMPACT This framework could improve the efficiency and effectiveness of retrieval systems in various applications, including e-commerce and recommendation engines.

RANK_REASON This is a research paper detailing a new framework for generative retrieval. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework enhances generative retrieval by using discarded quantization data

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This is a research paper detailing a new framework for generative retrieval. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Fuzhen Zhuang ·

    Residual Trajectory Distillation for Generative Retrieval

    Generative retrieval has emerged as a general retrieval paradigm, representing items with discrete Semantic IDs (SIDs) and retrieving them through autoregressive identifier generation. When SIDs are constructed with residual quantization (RQ), standard retrieval training supervis…

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