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Prompt Variability Effects On LLM Code Generation
Andrei Paleyes, Radzim Sendyka, Diana Robinson, Christian Cabrera, Neil D. Lawrence
https://arxiv.org/abs/2506.10204

Prompt Variability Effects On LLM Code Generation
Code generation is one of the most active areas of application of Large Language Models (LLMs). While LLMs lower barriers to writing code and accelerate development process, the overall quality of generated programs depends on the quality of given prompts. Specifically, functionality and quality of generated code can be sensitive to user's background and familiarity with software development. It is therefore important to quantify LLM's sensitivity to variations in the input. To this end we prop…

Overcoming Data Scarcity in Scanning Tunnelling Microscopy Image Segmentation
Nikola L. Kolev, Max Trouton, Filippo Federici Canova, Geoff Thornton, David Z. Gao, Neil J. Curson, Taylor J. Z. Stock
https://arxiv.org/abs/2506.01678

Overcoming Data Scarcity in Scanning Tunnelling Microscopy Image Segmentation
Scanning tunnelling microscopy (STM) is a powerful technique for imaging surfaces with atomic resolution, providing insight into physical and chemical processes at the level of single atoms and molecules. A regular task of STM image analysis is the identification and labelling of features of interest against a uniform background. Performing this manually is a labour-intensive task, requiring significant human effort. To reduce this burden, we propose an automated approach to the segmentation of…