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Taobao Search deploys Prompt Generation framework for generative retrieval

By PulseAugur Editorial · [1 sources] ·

A new framework called Prompt Generation (PG) has been introduced to decouple feature processing from model architecture in generative retrieval systems. This approach uses declarative JSON files as a single source of truth for both offline training and online serving, aiming to speed up iteration and deployment. PG has been implemented on Taobao Search, resulting in measurable online improvements in transaction count and gross merchandise volume, and is being adopted by other teams within Taobao for generative retrieval. AI

IMPACT Decouples feature engineering from model architecture, potentially accelerating development and deployment cycles for generative retrieval systems.

RANK_REASON The item is a technical report detailing a new framework for information retrieval systems, published on arXiv. [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 →

Taobao Search deploys Prompt Generation framework for generative retrieval

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The item is a technical report detailing a new framework for information retrieval systems, published on arXiv. [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) · Zhibo Wu ·

    Prompt Generation Technical Report

    Generative retrieval has become an increasingly adopted paradigm for industrial search, recommendation, and advertising systems, delivering significant online gains. Most existing work combines user behavior sequences with large language models (LLMs) to model user preferences. I…

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