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ConfReady: A RAG based Assistant and Dataset for Conference Checklist Responses

2024/08/07 by Michael Galarnyk, Rutwik Routu, Galarnyk, Michael +9 · 1 citation
Computer Science · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Software Engineering Techniques and Practices #Software System Performance and Reliability #Software Testing and Debugging Techniques

paper · pdf · doi:10.48550/arxiv.2408.04675

openalex publication_date 2024/08/07 · openalex created_date 2024/09/10 · openalex updated_date 2026/07/28

Abstract

The ARR Responsible NLP Research checklist website states that the "checklist is designed to encourage best practices for responsible research, addressing issues of research ethics, societal impact and reproducibility." Answering the questions is an opportunity for authors to reflect on their work and make sure any shared scientific assets follow best practices. Ideally, considering a checklist before submission can favorably impact the writing of a research paper. However, previous research has shown that self-reported checklist responses don't always accurately represent papers. In this work, we introduce ConfReady, a retrieval-augmented generation (RAG) application that can be used to empower authors to reflect on their work and assist authors with conference checklists. To evaluate checklist assistants, we curate a dataset of 1,975 ACL checklist responses, analyze problems in human answers, and benchmark RAG and Large Language Model (LM) based systems on an evaluation subset. Our code is released under the AGPL-3.0 license on GitHub, with documentation covering the user interface and PyPI package.

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