Task-Aware Retrieval with Small Language Models for Science
A recent study questions the need for large language models in the realm of scientific knowledge discovery, introducing a streamlined retrieval-augmented framework that utilizes smaller language models. This framework executes task-aware routing to identify tailored retrieval methods according to input queries, combining evidence from complete texts and structured metadata. By using compact instruction-tuned models, it generates responses complete with citations, with the goal of enhancing reproducibility and accessibility. Evaluations across various scholarly tasks indicate that well-crafted retrieval pipelines can effectively offset the limitations of smaller model sizes.
Key facts
- arXiv:2604.01965v2 is a paper on scientific knowledge discovery.
- The paper questions the need for large language models in science.
- It proposes a task-aware retrieval-augmented framework.
- The framework uses small language models.
- It performs task-aware routing for specialized retrieval.
- It integrates evidence from full-text papers and scholarly metadata.
- It generates responses with citations using compact instruction-tuned models.
- The framework was evaluated across several scholarly tasks.
Entities
Institutions
- arXiv