Automated Focused Feedback Generation for Scientific Writing Assistance

Scientific writing is a challenging task, particularly for novice researchers who often rely on feedback from experienced peers. Recent work has primarily focused on improving surface form and style rather than manuscript content. In this paper, we propose a novel task: automated focused feedback generation for scientific writing assistance 1 . We present SWIF 2 T: a Scientific WrIting Focused Feedback Tool. It is designed to generate specific, actionable and coherent comments, which identify weaknesses in a scientific paper and/or propose revisions to it. Our approach consists of four componentsplanner, investigator, reviewer and controllerleveraging multiple Large Language Models (LLMs) to implement them. We compile a dataset of 300 peer reviews citing weaknesses in scientific papers and conduct human evaluation. The results demonstrate the superiority in specificity, reading comprehension, and overall helpfulness of SWIF 2 T's feedback compared to other approaches. In our analysis, we also identified cases where automatically generated reviews were judged better than human ones, suggesting opportunities for integration of AIgenerated feedback in scientific writing. * The GF-SVM approach is an algorithm that combines the Genetic Folding (GF) algorithm with Support Vector Machines (SVM) for classifying patients with prostate cancer. It is a hybrid model that uses the SVM classifier with various conventional kernels to achieve high accuracy in classification. How is the GF-SVM approach applied to the prostate cancer detection dataset? * The GF-SVM approach is applied to the prostate cancer detection dataset by using the SVM classifier with several conventional kernels such as linear, polynomial, and RBF kernels. The performance of the GF-SVM approach is evaluated and compared to other ML approaches, and it is found to have superior accuracy. What improvements did the GF-SVM approach bring to the existing models in prostate cancer detection? * The GF-SVM approach brought superior accuracy performance compared to the six ML approaches in prostate cancer detection. What are the full features of the dataset used in the experiments? * The full features of the dataset used in the experiments are Radius, Texture, Perimeter, Area, Smoothness, Compactness, Symmetry, Fractal_dimension, and Diagnosis. What are the usual performance measures in prostate cancer detection models? * The usual performance measures in prostate cancer detection models are sensitivity, specificity, and the area under the ROC curve. Are there any specific considerations or challenges in handling datasets for cancer detection? * Yes, there are specific considerations and challenges in handling datasets for cancer detection, such as batch effects and the need to consider vital biological information.