Prompt-based methods are becoming increasingly relevant in biomedical text mining, offering flexible ways to perform tasks such as named entity recognition without explicit model training. In this case study, we assess the performance of DeepSeek-V3 on a structured disease mention extraction task using a curated subset of the NCBI Disease
How well can today’s leading GenAI models classify real-world biodiversity data—without bespoke code or traditional machine learning pipelines? In this study, we benchmarked a range of large language models on the task of predicting penguin species from tabular ecological measurements, including both numerical and categorical variables. Using a
How far can today’s large language models go in scientific data analysis—without bespoke coding or deep learning pipelines? In this experiment, we explore the ability of DeepSeek-V3 to perform pixel-level detection of Argania trees (i.e., binary classification for each pixel) using only tabular Sentinel-2 spectral data. By
The question of how alignment procedures shape the behaviour of large language models is not new. However, the release of DeepSeek’s R1 and V3 models has brought renewed attention to the issue by making alignment effects unusually observable. The model exhibits a clear divergence in output depending on the
Can a language model accurately classify argumentative claims without any prior examples or fine-tuning? We put DeepSeek-V3 to the test on a real-world stance classification task involving 200 claims from a structured dataset. The model was asked to determine, for each claim, whether it supported (PRO) or opposed (CON) a
