Improving embedding with contrastive fine-tuning on small datasets with expert-augmented scores
<|reference_start|>Improving embedding with contrastive fine-tuning on small datasets with expert-augmented scores: This paper presents an approach to improve text embedding models through contrastive fine-tuning on small datasets augmented with expert scores. It focuses on enhancing semantic textual similarity tasks and addressing text retrieval problems. The proposed method uses soft labels derived from expert-augmented scores to fine-tune embedding models, preserving their versatility and ensuring retrieval capability is improved. The paper evaluates the method using a Q\&A dataset from an online shopping website and eight expert models. Results show improved performance over a benchmark model across multiple metrics on various retrieval tasks from the massive text embedding benchmark (MTEB). The method is cost-effective and practical for real-world applications, especially when labeled data is scarce.<|reference_end|>
@article{lu2024improving,
    title={Improving embedding with contrastive fine-tuning on small datasets with
  expert-augmented scores},
    author={Jun Lu, David Li, Bill Ding, Yu Kang},
    journal={arXiv preprint arXiv:2408.11868},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.11868},
    primaryClass={cs.CL cs.AI cs.LG}
}