How to Read an AI Research Paper as a Non-Expert

Begin with your reason for reading: understanding an idea, checking a claim or deciding whether to try a method. Read the abstract, introduction, section headings and conclusion to identify the problem and claimed contribution. S. Keshav’s reading guide proposes progressively deeper passes rather than forcing a complete line-by-line reading immediately. Use the first pass to decide what deserves closer attention, not to certify correctness. On a closer pass, inspect the methods, figures and results. Identify the data, task, evaluation split, metric and comparison systems. A baseline is a reference method used for comparison. An ablation removes or changes a component to investigate its contribution; ask what else changed before attributing an outcome to that component. Read figure axes, table notes and definitions instead of relying on a bold score. Consider an invented paper reporting 91% accuracy for a new system and 90% for a baseline. That is a one-percentage-point difference, not proof of a reliable or useful advantage. Check sample size, variation across runs, comparable settings, failure cases and operating cost. A result on one dataset does not establish performance in every language, population or deployment. Read limitations and distinguish achieved results from aspirations. The NeurIPS checklist provides questions about claim scope, assumptions, reproducibility and uncertainty; it is a conference resource, not a universal certification. Hosting also has limits: arXiv explicitly distinguishes moderation from peer review. Check publication and revision records separately. If a proof or experimental detail remains unclear, mark that gap and consult the cited background or a knowledgeable reader. A useful summary states what was tested, what the evidence supports and what remains unknown.