Small Language Models Can Use Nuanced Reasoning For Health Science Research Classification: A Microbial-Oncogenesis Case Study

Abstract

Artificially intelligent (AI) co-scientists must be able to sift through research literature cost-efficiently while applying nuanced scientific reasoning. We evaluate Small Language Models (SLMs, <= 8B parameters) for classifying medical research papers. Using literature on the oncogenic potential of HMTV/MMTV-like viruses in breast cancer as a case study, we assess model performance with both zero-shot and in-context learning (ICL; few-shot prompting) strategies against frontier proprietary Large Language Models (LLMs). Llama 3 and Qwen2.5 outperform GPT-5 (API, low/high effort), Gemini 3 Pro Preview, and Meerkat in zero-shot settings, though trailing Gemini 2.5 Pro. ICL leads to improved performance on a case-by-case basis, allowing Llama 3 and Qwen2.5 to match Gemini 2.5 Pro in binary classification. Systematic lexical-ablation experiments show that SLM decisions are often grounded in valid scientific cues but can be influenced by spurious textual artifacts, underscoring need for interpretability in high-stakes pipelines. Our results reveal both promise and limitations of modern SLMs for scientific triage; pairing SLMs with simple but principled prompting strategies can approach performance of the strongest LLMs for targeted literature filtering in co-scientist pipelines.

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