ml-paper-writing

Drafting a machine learning paper for conference submission involves generating paper sections based on a research repository, structuring arguments, and verifying citations programmatically. This process streamlines writing for conferences like NeurIPS, ICML, and ICLR while adhering to official LaTeX templates.

Citation verification when writing an ML paper works by programmatically fetching accurate citations using APIs like Semantic Scholar, arXiv, and habanero. This mechanism prevents citation hallucinations and ensures research publication references are valid and correctly linked to academic sources.

Yes, you can use official LaTeX templates for NeurIPS or ICML conference formatting. The process utilizes official templates and adheres to specific conference requirements, ensuring your research publication meets the strict structural and stylistic guidelines of top-tier AI conferences.

Structuring arguments in an AI research publication is best achieved by drafting sections from your research repository and iterating on feedback. This approach provides expert writing guidance to logically align your experimental results with the core claims of your ML paper.

You do not strictly need a GitHub repository to start writing an ML paper, but providing a link to your research repository allows the drafting process to generate paper sections directly from your code and experimental context, significantly accelerating the initial writing phase.

Fetching citations programmatically prevents hallucinations in academic writing by querying validated academic databases like Semantic Scholar and arXiv instead of generating references from scratch. This ensures every citation in your ML paper points to an actual, verifiable publication.