Large Language Models Based Stemming for Information Retrieval: Promises, Pitfalls and Failures-Reference-Cited by-同舟云学术

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Large Language Models Based Stemming for Information Retrieval: Promises, Pitfalls and Failures

Published:2024-07-10 Issue: Volume: Page:2492-2496
ISSN:
Container-title:Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval
language:
Short-container-title:

Author:

Wang Shuai¹^ORCID,Zhuang Shengyao²^ORCID,Zuccon Guido¹^ORCID

Affiliation:

1. The University of Queensland Brisbane, QLD, Australia

2. CSIRO, Brisbane, QLD, Australia

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3626772.3657949

Reference36 articles.

1. Beeching, E., Fourrier, C., Habib, N., Han, S., Lambert, N., Rajani, N., Sanseviero, O., Tunstall, L., Wolf, T.: Open llm leaderboard. https://huggingface.co/spaces/ HuggingFaceH4/open_llm_leaderboard (2023)

2. InPars: Unsupervised Dataset Generation for Information Retrieval

3. A Survey on Evaluation of Large Language Models

4. Croft, B., Metzler, D., Strohman, T.: Search Engines: Information Retrieval in Practice. Addison-Wesley Publishing Company, USA, 1st edn. (2009)

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