Skill UI

A comprehensive guide for researchers designing and auditing NLP experiments for top-tier conferences. It covers establishing defensible baselines, ensuring evaluation breadth, performing rigorous statistical tests (significance, variance), controlling for data contamination, and structuring detailed functional error analysis. Essential reading for publishing reliable AI research.

This comprehensive guide provides expert strategies for structuring and auditing the "Related Work" section for top-tier NLP conferences (like ACL). It covers citation hygiene (Anthology vs. arXiv), maintaining temporal freshness against rapidly evolving baselines, and articulating clear technical contrasts to prove the novelty of your research. Essential for high-stakes academic publishing.

A comprehensive guide detailing the standards required for academic reproducibility in NLP and LLM research. It covers the 'Responsible NLP checklist' requirements, including detailed reporting for hyperparameters, model versions, exact prompts, decoding parameters, data contamination auditing, and variance reporting to ensure claims are auditable and robust.

A comprehensive guide to modeling the entire academic review cycle for major NLP conferences like ACL. It details the stages from initial submission (ARR cycle intake) through peer review, author response, meta-review synthesis, commitment decision, and final venue decision (Main Conference, Findings, or Reject). It also outlines the roles of reviewers, area chairs, and program chairs, providing strategic advice for authors on navigating the complex publication landscape.

A comprehensive audit checklist for authors submitting papers to major NLP conferences (like ACL) via OpenReview. This guide covers mandatory formatting rules, maintaining anonymity, addressing ethical declarations (Responsible NLP), managing resubmission links, and ensuring adherence to the entire submission workflow to prevent desk rejection.

A comprehensive guide for authors preparing submissions for ACL conferences. It details how to effectively split content across the main body, required Limitations section, optional Ethics statement, and supplementary archives. Learn best practices for ensuring critical findings remain visible to reviewers, maximizing reproducibility, and structuring appendices for maximum impact.

A comprehensive guide for NLP researchers on strategically positioning their work before writing. It teaches how to determine the core linguistic contribution, select the optimal academic venue (ACL, EMNLP, etc.), manage the scope (long vs. short papers), and align the research focus with specific conference themes and review expectations, maximizing publication impact.

This comprehensive guide provides a project management framework for publishing academic papers at major conferences like ACL. It details the complex "cycle-and-commit" rhythm, covering milestones from initial ARR submission through review, meta-review, commitment, camera-ready preparation, and final presentation. Learn techniques for backward planning, managing resubmission loops, and coordinating multiple papers across volatile academic cycles.

A comprehensive guide for revising NLP and Computational Linguistics manuscripts for submission to top-tier conferences like ACL. It mandates adopting a task-first, evidence-grounded approach, strictly scoping claims in the LLM era, and ensuring linguistic rigor in all examples and analyses. Learn how to structure, compress, and refine your arguments for maximum impact.

A comprehensive guide for researchers to determine if their work represents a genuine cross-modal or media-systems contribution suitable for ACM Multimedia (ACM MM). It helps route projects by testing for cross-modality, media system advances, or human-centric results, distinguishing between pure CV, NLP, or system-heavy claims.

A comprehensive guide for authors submitting papers to the ACL conference. It assists in positioning the manuscript, verifying topic fit (NLP, language models, semantics), and ensuring the evidence meets the high standards required for top-tier computational linguistics venues. Covers structure, best practices, and submission checklists.

A comprehensive guide for researchers and reviewers designing or auditing Automated Software Engineering (ASE) evaluations. It outlines the necessary rigor, including the use of real subject systems, establishing fair and runnable tool baselines, performing targeted ablations (especially for LLMs), defining explicit oracles, and ensuring full data and model provenance to guarantee reproducible and scientifically sound results.