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

TL;DR

ConfReady is a RAG-based tool designed to assist authors in completing conference checklists, promoting responsible research practices and improving the accuracy of self-reported checklist responses.

Contribution

This work introduces ConfReady, a novel RAG-based application and dataset for enhancing conference checklist responses and assessing related challenges.

Findings

Benchmarking of RAG and LM systems on checklist responses

Analysis of issues in human-provided checklist answers

Curated dataset of 1,975 ACL checklist responses

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.