arXiv:2606.24667v1 Announce Type: new Abstract: Dense retrieval embedding models are a fundamental component of modern retrieval-based AI systems. Most dense retrievers are trained with contrastive objectives, which require labeled positive and negative document pairs that are often costly and difficult to obtain. In this work, we investigate whether the autoregressive next-token prediction objective of a large language model (LLM) can provide supervision for dense retrieval. The intuition is simple: if a document contains information relevant to a query, conditioning on that document should m
The explosion of large language models (LLMs) has opened new avenues for leveraging their intrinsic capabilities, such as next-token prediction, to improve other AI components like dense retrieval models without expensive, specialized datasets.
This research outlines a method to train dense retrieval models more efficiently and with less labeled data, which can significantly lower the barrier to entry and accelerate the development of retrieval-based AI systems across various applications.
The reliance on costly and difficult-to-obtain labeled positive and negative document pairs for training dense retrieval models may decrease, potentially leading to more robust and scalable retrieval systems.
- · AI developers
- · Companies building retrieval-augmented generation (RAG) systems
- · Academic AI researchers
- · Providers of expensive, specialized labeled datasets for retrieval
More efficient and powerful large language models and retrieval systems become widely available.
Improved information retrieval capabilities lead to more accurate AI assistants and knowledge management systems.
Democratization of sophisticated AI tools reduces the competitive advantage of companies with vast proprietary labeled datasets, shifting focus to model architecture and training innovation.
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