Skill UI

This guide provides strategic advice on drafting author responses for major NLP conferences (like EMNLP/ACL ARR). It teaches authors how to triage reviewer comments by soundness, excitement, and misreading, structure replies for maximum impact on the Area Chair's meta-review, and manage conflicts between reviewers. Learn what evidence can be added mid-review and how to strategically concede.

This comprehensive guide outlines the rigorous standards for designing, auditing, and reporting Natural Language Processing (NLP) experiments, specifically tailored for top-tier academic conferences like EMNLP. It covers critical areas including baseline fairness checks, data contamination audits, statistical significance testing, prompt sensitivity analysis, and structured human evaluation protocols, ensuring reported claims are robust, reproducible, and methodologically sound.

A deep dive into the best practices for drafting the Related Work section for top-tier NLP conferences (like EMNLP). This guide instructs authors on how to trace task lineage, verify dataset ancestry, manage concurrent work citations (arXiv vs. Anthology), and ensure rigorous citation hygiene to withstand expert reviewer scrutiny.

This guide details how to prepare high-quality, verifiable system artifacts (code, data, hardware) for academic conferences like EuroSys. It outlines the requirements for achieving multiple evaluation badges—Available, Functional, and Reproduced—by ensuring the artifact is self-contained, portable, and verifiable by an external evaluator without prior context.

A comprehensive strategic guide for authors responding to peer reviews at top-tier academic conferences. It teaches how to interpret reviews as a 'bin-sorting' problem, structure persuasive rebuttals, address specific technical objections (measurement, design), and convert vague revision conditions into a concrete, actionable resubmission contract and plan.

This guide meticulously models the rigorous decision-making machinery of top-tier academic conferences, using EuroSys as a detailed example. It covers the two-round submission cycle, the 'Accept/Revise/Reject' outcome logic, double-blind review mechanics, and critical criteria (like deployment plausibility and verifiable fairness) that determine a paper's success. Ideal for researchers and graduate students preparing systems research submissions.

A comprehensive guide detailing how to structure and submit highly technical papers to major academic conferences (e.g., EuroSys). It outlines the 'four shelves'—the 12-page core argument, references, artifact repositories, and appendices—ensuring that the main, decision-critical evidence is always contained within the primary reviewed pages, preventing the submission from failing due to overflow.

A comprehensive guide for drafting and revising top-tier systems papers, specifically for conferences like EuroSys. It details the required academic structure, including building the problem-to-mechanism arc, managing the 12-page technical budget, writing rigorous evaluation sections (including measuring failure cases), and maintaining the necessary double-blind academic voice.

This guide provides best practices for authors writing the "Related Work" section for interdisciplinary AI conferences. It teaches how to precisely position novelty across multiple domains—including ML fairness, law, HCI, and critical theory—by writing delta-first contrasts and properly attributing borrowed concepts to their original academic source.

A comprehensive project management guide for submitting interdisciplinary, responsible AI papers to major academic conferences like FAccT. It provides a detailed backward-planning timeline, covering milestones from initial abstract registration and literature review to revisions, camera-ready submission, and conference presentation, emphasizing ethical considerations and structured academic processes.

A comprehensive guide for authors responding to technical reviews at top-tier conferences. It differentiates between the initial rebuttal and the final change ledger, providing best practices for addressing reviewer concerns regarding measurement soundness, consistency, and performance. Focuses heavily on maintaining double-blind anonymity while strengthening technical arguments.

This guide provides comprehensive best practices for writing the 'Related Work' section for submissions to top-tier storage conferences like FAST. It emphasizes adopting a 'delta-first' contrast approach, requiring authors to position their novelty against the entire spectrum of storage literature (including OSDI, SOSP, and dedicated workshops), rather than merely citing proximate works. It also covers handling concurrent submissions and maintaining double-blind anonymity.