NUDGE: Lightweight Non-Parametric Fine-Tuning of Embeddings for Retrieval

• URL: http://arxiv.org/abs/2409.02343v1
• Date: Wed, 4 Sep 2024 00:10:36 GMT
• Title: NUDGE: Lightweight Non-Parametric Fine-Tuning of Embeddings for Retrieval
• Authors: Sepanta Zeighami, Zac Wellmer, Aditya Parameswaran,
• Abstract summary: Existing approaches either fine-tune the pre-trained model itself or, more efficiently, train adaptor models to transform the output of the pre-trained model. We present NUDGE, a family of novel non-parametric embedding fine-tuning approaches. NUDGE directly modifies the embeddings of data records to maximize the accuracy of $k$-NN retrieval.
• Score: 0.7646713951724011
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: $k$-Nearest Neighbor search on dense vector embeddings ($k$-NN retrieval) from pre-trained embedding models is the predominant retrieval method for text and images, as well as Retrieval-Augmented Generation (RAG) pipelines. In practice, application developers often fine-tune the embeddings to improve their accuracy on the dataset and query workload in hand. Existing approaches either fine-tune the pre-trained model itself or, more efficiently, but at the cost of accuracy, train adaptor models to transform the output of the pre-trained model. We present NUDGE, a family of novel non-parametric embedding fine-tuning approaches that are significantly more accurate and efficient than both sets of existing approaches. NUDGE directly modifies the embeddings of data records to maximize the accuracy of $k$-NN retrieval. We present a thorough theoretical and experimental study of NUDGE's non-parametric approach. We show that even though the underlying problem is NP-Hard, constrained variations can be solved efficiently. These constraints additionally ensure that the changes to the embeddings are modest, avoiding large distortions to the semantics learned during pre-training. In experiments across five pre-trained models and nine standard text and image retrieval datasets, NUDGE runs in minutes and often improves NDCG@10 by more than 10% over existing fine-tuning methods. On average, NUDGE provides 3.3x and 4.3x higher increase in accuracy and runs 200x and 3x faster, respectively, over fine-tuning the pre-trained model and training adaptors.

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