Large Language Models as Foundations for Next-Gen Dense Retrieval: A Comprehensive Empirical Assessment
Kun Luo, Minghao Qin, Zheng Liu, Shitao Xiao, Jun Zhao, Kang Liu
Abstract
Pre-trained language models like BERT and T5 serve as crucial backbone encoders for dense retrieval. However, these models often exhibit limited generalization capabilities and face challenges in improving in-domain accuracy. Recent research has explored using large language models (LLMs) as retrievers, achieving state-of-the-art performance across various tasks. Despite these advancements, the specific benefits of LLMs over traditional retrievers and the impact of different LLM configurations—such as parameter sizes, pre-training duration, and alignment processes—on retrieval tasks remain unclear. In this work, we conduct a comprehensive empirical study on a wide range of retrieval tasks, including in-domain accuracy, data efficiency, zero-shot generalization, lengthy retrieval, instruction-based retrieval, and multi-task learning. We evaluate over 15 different backbone LLMs and non-LLMs. Our findings reveal that larger models and extensive pre-training consistently enhance in-domain accuracy and data efficiency. Additionally, larger models demonstrate significant potential in zero-shot generalization, lengthy retrieval, instruction-based retrieval, and multi-task learning. These results underscore the advantages of LLMs as versatile and effective backbone encoders in dense retrieval, providing valuable insights for future research and development in this field.- Anthology ID:
- 2024.emnlp-main.80
- Volume:
- Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
- Month:
- November
- Year:
- 2024
- Address:
- Miami, Florida, USA
- Editors:
- Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1354–1365
- Language:
- URL:
- https://aclanthology.org/2024.emnlp-main.80
- DOI:
- 10.18653/v1/2024.emnlp-main.80
- Cite (ACL):
- Kun Luo, Minghao Qin, Zheng Liu, Shitao Xiao, Jun Zhao, and Kang Liu. 2024. Large Language Models as Foundations for Next-Gen Dense Retrieval: A Comprehensive Empirical Assessment. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 1354–1365, Miami, Florida, USA. Association for Computational Linguistics.
- Cite (Informal):
- Large Language Models as Foundations for Next-Gen Dense Retrieval: A Comprehensive Empirical Assessment (Luo et al., EMNLP 2024)
- PDF:
- https://preview.aclanthology.org/landing_page/2024.emnlp-main.80.pdf