Abstract
Scientific writing is a challenging task, particularly for novice researchers who often rely on feedback from experienced peers. Recent work has primarily focused on improving surface form and style rather than manuscript content. In this paper, we propose a novel task: automated focused feedback generation for scientific writing assistance. We present SWIF2T: a Scientific WrIting Focused Feedback Tool. It is designed to generate specific, actionable and coherent comments, which identify weaknesses in a scientific paper and/or propose revisions to it. Our approach consists of four components - planner, investigator, reviewer and controller - leveraging multiple Large Language Models (LLMs) to implement them. We compile a dataset of 300 peer reviews citing weaknesses in scientific papers and conduct human evaluation. The results demonstrate the superiority in specificity, reading comprehension, and overall helpfulness of SWIF2T’s feedback compared to other approaches. In our analysis, we also identified cases where automatically generated reviews were judged better than human ones, suggesting opportunities for integration of AI-generated feedback in scientific writing.- Anthology ID:
- 2024.findings-acl.580
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2024
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Lun-Wei Ku, Andre Martins, Vivek Srikumar
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 9742–9763
- Language:
- URL:
- https://aclanthology.org/2024.findings-acl.580
- DOI:
- 10.18653/v1/2024.findings-acl.580
- Cite (ACL):
- Eric Chamoun, Michael Schlichtkrull, and Andreas Vlachos. 2024. Automated Focused Feedback Generation for Scientific Writing Assistance. In Findings of the Association for Computational Linguistics: ACL 2024, pages 9742–9763, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- Automated Focused Feedback Generation for Scientific Writing Assistance (Chamoun et al., Findings 2024)
- PDF:
- https://preview.aclanthology.org/autopr/2024.findings-acl.580.pdf