arXiv:2601.12910v2 Announce Type: replace-cross
Abstract: We present SciCoQA, a dataset for detecting discrepancies between scientific publications and their codebases to ensure faithful implementations. We construct SciCoQA from GitHub issues and reproducibility papers, and to scale our dataset, we propose a synthetic data generation method for constructing paper-code discrepancies. We analyze the paper-code discrepancies in detail and propose discrepancy types and categories to better understand the occurring mismatches. In total, our dataset consists of 635 paper-code discrepancies (92 real, 543 synthetic), covering the AI domain from real-world data and extending to Physics, Quantitative Biology, and other computational sciences through synthetic data. Our evaluation of 22 LLMs demonstrates the difficulty of SciCoQA, particularly for instances involving omitted paper details, long-context inputs, and data outside the models’ pre-training corpus. The best-performing models in our evaluation, Gemini 3.1 Pro and GPT-5 Mini, detect only 46.7% of real-world paper-code discrepancies.
Depression subtype classification from social media posts: few-shot prompting vs. fine-tuning of large language models
BackgroundSocial media provides timely proxy signals of mental health, but reliable tweet-level classification of depression subtypes remains challenging due to short, noisy text, overlapping symptomatology,