Fine-Tuning LLaMA for Multi-Stage Text Retrieval: Conclusion, Acknowledgements and References

Table of Links

Abstract and Introduction

Method

Experiments

Ablation Study and Analysis

Related Work

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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