Beyond Contrastive Learning: Synthetic Data Enables List-wise Training with Multiple Levels of Relevance

• URL: http://arxiv.org/abs/2503.23239v1
• Date: Sat, 29 Mar 2025 22:33:22 GMT
• Title: Beyond Contrastive Learning: Synthetic Data Enables List-wise Training with Multiple Levels of Relevance
• Authors: Reza Esfandiarpoor, George Zerveas, Ruochen Zhang, Macton Mgonzo, Carsten Eickhoff, Stephen H. Bach,
• Abstract summary: In this work we forgo real training documents and annotations altogether.<n>We use open-source LLMs to directly generate synthetic documents that answer real user queries according to several different levels of relevance.<n> Experiments on various IR datasets show that our proposed approach outperforms conventional training with InfoNCE by a large margin.
• Score: 24.842839260409075
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advancements in large language models (LLMs) have allowed the augmentation of information retrieval (IR) pipelines with synthetic data in various ways. Yet, the main training paradigm remains: contrastive learning with binary relevance labels and the InfoNCE loss, where one positive document is compared against one or more negatives. This objective treats all documents that are not explicitly annotated as relevant on an equally negative footing, regardless of their actual degree of relevance, thus (a) missing subtle nuances that are useful for ranking and (b) being susceptible to annotation noise. To overcome this limitation, in this work we forgo real training documents and annotations altogether and use open-source LLMs to directly generate synthetic documents that answer real user queries according to several different levels of relevance. This fully synthetic ranking context of graduated relevance, together with an appropriate list-wise loss (Wasserstein distance), enables us to train dense retrievers in a way that better captures the ranking task. Experiments on various IR datasets show that our proposed approach outperforms conventional training with InfoNCE by a large margin. Without using any real documents for training, our dense retriever significantly outperforms the same retriever trained through self-supervision. More importantly, it matches the performance of the same retriever trained on real, labeled training documents of the same dataset, while being more robust to distribution shift and clearly outperforming it when evaluated zero-shot on the BEIR dataset collection.

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