Do We Need Bigger Models for Science? Task-Aware Retrieval with Small Language Models
Hey there, little scientist! 🚀
Imagine you have a super-duper smart robot friend. This robot helps grown-ups learn new things about the world, like how plants grow or why stars twinkle.
Sometimes, these robots are super-duper big, like a giant castle! They know everything because they've read all the books. But big castles are hard to move around.
This story is about asking: "Do we always need a giant castle robot?" 🤔
Maybe we can have a smaller, clever robot! This clever robot has a special trick: it knows exactly where to look in its library of books to find the right answer super fast. It's like having a magic map to find the treasure! 🗺️✨
So, the grown-ups found out that sometimes, a small, clever robot with a good map can do almost as good a job as the giant castle robot! Isn't that cool? 🎉
arXiv:2604.01965v1 Announce Type: new 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 an
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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.
Comments: Accepted at NSLP@LREC 2026
Subjects:
Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Digital Libraries (cs.DL)
Cite as: arXiv:2604.01965 [cs.IR]
(or arXiv:2604.01965v1 [cs.IR] for this version)
https://doi.org/10.48550/arXiv.2604.01965
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Michael Färber [view email] [v1] Thu, 2 Apr 2026 12:28:51 UTC (252 KB)
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