PEFA: Parameter-Free Adapters for Large-scale Embedding-based Retrieval Models

Author:

Chang Wei-Cheng1ORCID,Jiang Jyun-Yu2ORCID,Zhang Jiong2ORCID,Al-Darabsah Mutasem2ORCID,Teo Choon Hui2ORCID,Hsieh Cho-Jui3ORCID,Yu Hsiang-Fu2ORCID,Vishwanathan S. V. N.2ORCID

Affiliation:

1. Amazon, Seattle, WA, USA

2. Amazon, Palo Alto, CA, USA

3. UCLA, Los Angeles, CA, USA

Publisher

ACM

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3. Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai, Eliza Rutherford, Katie Millican, George Bm Van Den Driessche, Jean-Baptiste Lespiau, Bogdan Damoc, Aidan Clark, et al. 2022. Improving language models by retrieving from trillions of tokens. In International conference on machine learning. PMLR, 2206--2240.

4. Tom Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared D Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell et al. 2020. Language models are few-shot learners. Advances in neural information processing systems Vol. 33 (2020) 1877--1901.

5. Extreme Multi-label Learning for Semantic Matching in Product Search

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