Efficient fine-tuning methodology of text embedding models for information retrieval: contrastive learning penalty (clp)

Abstract: 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.

• The paper introduces a novel contrastive learning penalty that refines the impact of negative samples in text embedding training.
• It employs the ANCE technique for effective data selection, reducing gradient variance and boosting retrieval performance.
• The integration of mixture of experts enhances model specialization and achieves an approximate 5-point nDCG increase across diverse languages.

Efficient Fine-tuning Methodology of Text Embedding Models for Information Retrieval: Contrastive Learning Penalty (CLP)

The research paper "Efficient Fine-tuning Methodology of Text Embedding Models for Information Retrieval: Contrastive Learning Penalty (CLP)" presents a series of methodological advancements aimed at enhancing the performance of pre-trained text embedding models specifically for information retrieval tasks. The proposed methodologies are focused on refining the training data selection, introducing a novel loss function called Contrastive Learning Penalty (CLP), and optimizing model architecture with Mixture of Experts (MoE).

The study positions itself at the intersection of NLP and information retrieval (IR), addressing the deficiencies of traditional keyword-matching approaches like BM25, which suffer from vocabulary mismatch, difficulty in understanding semantics, and ignoring syntactic structures. By leveraging pre-trained text embedding models such as BGE M3-Embedding, the research capitalizes on dense retrieval's capability to semantically understand queries and documents, moving toward a vector-space model of text representation.

Core Contributions

1. Data Selection Technique: ANCE The study employs Approximate Nearest Neighbor Negative Contrastive Estimation (ANCE) for selecting negative samples during the training phase. By prioritizing informative negative samples, the training process benefits from a reduction in issues like decreased gradient values and increased stochastic gradient variance, which are often generated by the inclusion of distant, uninformative samples.
2. Contrastive Learning Penalty (CLP) Function The CLP is introduced as a refinement over standard contrastive learning approaches. Unlike traditional contrastive learning that only focuses on the proximity of positive and negative samples relative to a given query, the CLP also considers the distance between negative samples and their associated queries. This nuanced approach mitigates potential negative impacts on learning and achieved a notable increase in retrieval performance, particularly evident in languages like Persian, where standard methodologies faltered.
3. Mixture of Experts (MoE) Application Methodologies previously used dense networks with identical weight applications across tasks. The integration of MoE facilitates specialization by tailoring subsets of weights based on input characteristics. Deployment of MoE to the intermediate layers of text embedding models demonstrated an improvement in model generalization and performance, albeit with an increased inference time.

Experimental Validation

The proposed methodologies were validated through extensive experiments using the MIRACL dataset, assessed via the nDCG metric. The results highlighted strong performance across three linguistically diverse languages: Korean, Hindi, and Persian. The inclusion of all proposed techniques culminated in an approximate 5-point increase in nDCG scores compared to baseline models, underscoring the effectiveness of the proposed fine-tuning strategies.

Implications and Future Directions

The incorporation of CLP and MoE structures marks a significant methodological innovation in the fine-tuning of text embedding models for IR tasks. This approach not only demonstrates the potential for enhanced retrieval precision but also suggests avenues for future research in diverse domains. The study invites further exploration into the optimization of the CLP's computational efficiency and broader application of its methodologies across additional languages and domains.

The research offers a code repository for replication and further experimentation, enabling other researchers to build upon its findings. Future work could focus on extending CLP's applicability, evaluating cross-lingual performance, and optimizing processing time while maintaining or enhancing accuracy.

In conclusion, this study furnishes a robust, empirically validated enhancement pathway for improving information retrieval through sophisticated fine-tuning of text embedding models, contributing substantially to both the theoretical framework and practical capabilities of contemporary NLP systems.