GPT Academic

Four useful starting projects

• Read an unfamiliar paper: Generate a structured reading guide with the problem, method, evidence and limitations. Add page or section references manually where needed, then use the guide to prioritize which parts deserve a close reading.
• Prepare a bilingual paper draft: Translate a short section with a supplied glossary and compare the scientific claim sentence by sentence. Extend the workflow only after terminology, units and abbreviations are stable.
• Polish a LaTeX submission: Ask for language changes while preserving formulas, labels and citation commands. Review the diff and compile the project before accepting the edited version into a submission branch.
• Understand research software: Request a map of modules and the likely data flow from a small code repository. Check important statements against the source and actual execution before relying on the explanation in a report.

Platforms, inputs, outputs and setup

The project provides Python and container-oriented installation routes with configuration and plugin documentation in its wiki. Different tasks need different components: a plain text rewrite and a LaTeX translation project do not have identical environment requirements.

Model lists and tutorials can reflect the period in which they were written. Use the current provider documentation and project configuration for an active model, rather than assuming every historical model name in an example is still available.

Data, permissions and licensing

Determine where the configured model receives document text before processing unpublished papers or internal code. Use an approved local route or an authorized provider when the material cannot be sent to a public service.

Keep API credentials out of shared prompts, screenshots and repository commits. Generated summaries and translations should remain traceable to their originals; verify citations and preserve authorship rather than treating fluent output as newly established evidence.