Do We Need Bigger Models for Science? Task-Aware Retrieval with Small Language Models

TL;DR

This paper explores whether smaller, well-designed retrieval systems combined with compact language models can effectively replace larger models in scientific tasks, emphasizing retrieval and task-aware design for reproducibility.

Contribution

The authors propose a retrieval-augmented framework with task-aware routing and evidence integration, demonstrating its effectiveness across various scholarly question answering tasks.

Findings

Retrieval design can partially compensate for smaller models.

Model capacity remains crucial for complex reasoning tasks.

Retrieval and model scale are complementary factors.

Abstract

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 integrates evidence from full-text scientific papers and structured scholarly metadata, and employs compact instruction-tuned language models to generate responses with citations. We evaluate the framework across several scholarly tasks, focusing on scholarly question answering (QA), including single- and multi-document scenarios, as well as biomedical QA under domain shift and scientific text compression. Our findings demonstrate that retrieval and model scale are complementary rather than interchangeable. While retrieval design can partially compensate for smaller models, model capacity remains important for complex reasoning tasks. This work highlights retrieval and task-aware design as key factors for building practical and reproducible scholarly assistants.