Learning Effective Representations for Retrieval Using Self-Distillation with Adaptive Relevance Margins

• URL: http://arxiv.org/abs/2407.21515v1
• Date: Wed, 31 Jul 2024 10:33:32 GMT
• Title: Learning Effective Representations for Retrieval Using Self-Distillation with Adaptive Relevance Margins
• Authors: Lukas Gienapp, Niklas Deckers, Martin Potthast, Harrisen Scells,
• Abstract summary: biencoders estimate relevance of a document to a query by calculating the similarity of their respective embeddings. Current state-of-the-art biencoders are trained using an expensive training regime involving knowledge distillation from a teacher model and batch-sampling. We propose a novel parameter-free loss function for self-supervision that exploits the pre-trained language modeling capabilities of the encoder model as a training signal.
• Score: 29.88235846291593
• License:
• Abstract: Representation-based retrieval models, so-called biencoders, estimate the relevance of a document to a query by calculating the similarity of their respective embeddings. Current state-of-the-art biencoders are trained using an expensive training regime involving knowledge distillation from a teacher model and batch-sampling. Instead of relying on a teacher model, we contribute a novel parameter-free loss function for self-supervision that exploits the pre-trained language modeling capabilities of the encoder model as a training signal, eliminating the need for batch sampling by performing implicit hard negative mining. We investigate the capabilities of our proposed approach through extensive ablation studies, demonstrating that self-distillation can match the effectiveness of teacher distillation using only 13.5% of the data, while offering a speedup in training time between 3x and 15x compared to parametrized losses. Code and data is made openly available.

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