Generative Retrieval via Term Set Generation-Reference-Cited by-同舟云学术

Generative Retrieval via Term Set Generation

Published:2024-07-10 Issue: Volume:24 Page:458-468
ISSN:
Container-title:Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval
language:
Short-container-title:

Author:

Zhang Peitian¹^ORCID,Liu Zheng²^ORCID,Zhou Yujia¹^ORCID,Dou Zhicheng¹^ORCID,Liu Fangchao³^ORCID,Cao Zhao³^ORCID

Affiliation:

1. Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China

2. Beijing Academy of Artificial Intelligence, Beijing, China

3. Huawei Poisson Lab, Beijing, China

Funder

National Natural Science Foundation of China

Beijing Natural Science Foundation

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3626772.3657797

Reference61 articles.

1. Michele Bevilacqua Giuseppe Ottaviano Patrick Lewis Scott Yih Sebastian Riedel and Fabio Petroni. 2022. Autoregressive Search Engines: Generating Substrings as Document Identifiers. In Advances in Neural Information Processing Systems Alice H. Oh Alekh Agarwal Danielle Belgrave and Kyunghyun Cho (Eds.). https://openreview.net/forum?id=Z4kZxAjg8Y

2. Nicola De Cao, Gautier Izacard, Sebastian Riedel, and Fabio Petroni. 2021. Autoregressive Entity Retrieval. In 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021. OpenReview.net. https://openreview.net/forum?id=5k8F6UU39V

3. Jianlv Chen Shitao Xiao Peitian Zhang Kun Luo Defu Lian and Zheng Liu. 2023a. BGE M3-Embedding: Multi-Lingual Multi-Functionality Multi-Granularity Text Embeddings Through Self-Knowledge Distillation. arxiv: 2309.07597 [cs.CL]

4. A Unified Generative Retriever for Knowledge-Intensive Language Tasks via Prompt Learning