This content originally appeared on HackerNoon and was authored by Writings, Papers and Blogs on Text Models
:::info Authors:
(1) Xueguang Ma, David R. Cheriton School of Computer Science, University of Waterloo;
(2) Liang Wang, Microsoft Research;
(3) Nan Yang, Microsoft Research;
(4) Furu Wei, Microsoft Research;
(5) Jimmy Lin, David R. Cheriton School of Computer Science, University of Waterloo.
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Table of Links
Conclusion, Acknowledgements and References
6 Conclusion
The successful application of large language models in generative tasks has sparked interest in their potential to enhance retrieval. In this study, we demonstrate that it is possible to fine-tune a large model to act as a dense retriever (RepLLaMA) and a pointwise reranker (RankLLaMA), thereby establishing an effective, state-of-the-art multi-stage retrieval system that outperforms smaller models built on the same basic design. Moreover, our approach offers greater optimization and efficient inference potential than recent methods that prompt large language models for text reranking in a generative manner. This work underscores the potential of leveraging LLMs for retrieval tasks in the future, which we continue to explore.
Acknowledgments
This research was supported in part by the Natural Sciences and Engineering Research Council (NSERC) of Canada.
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:::info This paper is available on arxiv under CC 4.0 license.
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This content originally appeared on HackerNoon and was authored by Writings, Papers and Blogs on Text Models
Writings, Papers and Blogs on Text Models | Sciencx (2024-07-05T16:10:08+00:00) Fine-Tuning LLaMA for Multi-Stage Text Retrieval: Conclusion, Acknowledgements and References. Retrieved from https://www.scien.cx/2024/07/05/fine-tuning-llama-for-multi-stage-text-retrieval-conclusion-acknowledgements-and-references/
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