Do We Need Bigger Models for Science? Task-Aware Retrieval with Small Language Models
Scientific knowledge discovery increasingly relies on large language models, yet many existing scholarly assistants depend on proprietary systems with tens or hundreds of billions of parameters. Such reliance limits reproducibility and accessibility for the research community. In this work, we ask a simple question: do we need bigger models for scientific applications? Specifically, we investigate to what extent carefully designed retrieval pipelines can compensate for reduced model scale in scientific applications. We design a lightweight retrieval-augmented framework that performs task-aware routing to select specialized retrieval strategies based on the input query. The system further ...
| Authors | Florian Kelber, Matthias Jobst, Yuni Susanti et al. |
|---|---|
| Primary category | cs.IR |
| Published | 2 April 2026 |
| Last updated at source | 22 April 2026 |
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