Efficient fine-tuning methodology of text embedding models for information retrieval: contrastive learning penalty (clp)
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866909438568300544 |
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| author | Yu, Jeongsu |
| author_facet | Yu, Jeongsu |
| contents | Text embedding models play a crucial role in natural language processing, particularly in information retrieval, and their importance is further highlighted with the recent utilization of RAG (Retrieval- Augmented Generation). This study presents an efficient fine-tuning methodology encompassing data selection, loss function, and model architecture to enhance the information retrieval performance of pre-trained text embedding models. In particular, this study proposes a novel Contrastive Learning Penalty function that overcomes the limitations of existing Contrastive Learning. The proposed methodology achieves significant performance improvements over existing methods in document retrieval tasks. This study is expected to contribute to improving the performance of information retrieval systems through fine-tuning of text embedding models. The code for this study can be found at https://github.com/CreaLabs/Enhanced-BGE-M3-with-CLP-and-MoE, and the best-performing model can be found at https://huggingface.co/CreaLabs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_17364 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Efficient fine-tuning methodology of text embedding models for information retrieval: contrastive learning penalty (clp) Yu, Jeongsu Information Retrieval Artificial Intelligence 68T50, 68P20 H.3.3; I.2.7 Text embedding models play a crucial role in natural language processing, particularly in information retrieval, and their importance is further highlighted with the recent utilization of RAG (Retrieval- Augmented Generation). This study presents an efficient fine-tuning methodology encompassing data selection, loss function, and model architecture to enhance the information retrieval performance of pre-trained text embedding models. In particular, this study proposes a novel Contrastive Learning Penalty function that overcomes the limitations of existing Contrastive Learning. The proposed methodology achieves significant performance improvements over existing methods in document retrieval tasks. This study is expected to contribute to improving the performance of information retrieval systems through fine-tuning of text embedding models. The code for this study can be found at https://github.com/CreaLabs/Enhanced-BGE-M3-with-CLP-and-MoE, and the best-performing model can be found at https://huggingface.co/CreaLabs. |
| title | Efficient fine-tuning methodology of text embedding models for information retrieval: contrastive learning penalty (clp) |
| topic | Information Retrieval Artificial Intelligence 68T50, 68P20 H.3.3; I.2.7 |
| url | https://arxiv.org/abs/2412.17364 |