Automated Focused Feedback Generation for Scientific Writing Assistance (2405.20477v2)

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 SWIF$^{2}$T: 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 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 SWIF$^{2}$T'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.

• The paper introduces SWIF²T, an automated system that generates focused and actionable feedback by decomposing writing evaluation into planning, investigation, reviewing, and controlling stages.
• It leverages multiple large language models and a dataset of 2,581 reviews, employing a re-ranking mechanism to enhance feedback coherence and specificity.
• Human and automatic evaluations confirm SWIF²T's superiority over existing methods, highlighting its potential to improve peer review quality and reduce reviewer workload.

Automated Focused Feedback Generation for Scientific Writing Assistance

Overview

The paper, "Automated Focused Feedback Generation for Scientific Writing Assistance," presents a comprehensive approach named SWIF$^{2}$T (Scientific WrIting Focused Feedback Tool) aimed at enhancing the feedback mechanism in scientific writing. The core proposition is an automated system engineered to generate specific, actionable, and coherent feedback, fundamentally distinguished from existing tools that prioritize superficial enhancements over substantive critiques.

Components and Architecture

SWIF$^{2}$T leverages multiple LLMs to decompose feedback generation into four pivotal components:

1. Planner: Designs a step-by-step schema to acquire relevant context from the manuscript and literature.
2. Investigator: Executes queries on both the manuscript and external sources to gather data pertinent to the review.
3. Reviewer: Utilizes the collated data to identify weaknesses and propose improvements.
4. Controller: Oversees the execution of the plan, dynamically adapting it in response to intermediary results.

The architecture ensures that the feedback provided is deeply informed, contextually aware, and systematically derived.

Methodology

The authors compiled a dataset consisting of 2,581 peer reviews linked to specific paragraphs in scientific manuscripts, sourced from several established peer review databases. The development of SWIF$^{2}$T included training models for communicative purpose prediction and aspect-based annotation, with a specific focus on capturing weaknesses related to replicability, originality, empirical and theoretical soundness, meaningful comparison, and substance.

A notable feature of SWIF$^{2}$T is the plan re-ranking mechanism, which optimizes the generated plan based on structure, coherence, and specificity criteria. The authors conducted a rigorous training regimen for the re-ranker, which significantly enhances the quality and relevance of the feedback.

Evaluation and Results

Through both automatic and human evaluations, SWIF$^{2}$T demonstrated superiority in generating specific, readable, and actionable feedback compared to robust baselines like GPT-4 and CoVe. The human evaluation involved experienced researchers who rated the feedback on four criteria: specificity, actionability, reading comprehension, and overall helpfulness. SWIF$^{2}$T outperformed other models across all criteria, substantiating its efficacy in delivering valuable scientific feedback.

Strong Numerical Results

The numerical results from SWIF$^{2}$T underscore its superior performance:

• Human evaluations showed SWIF$^{2}$T achieving high dominance scores in specificity (170.50), reading comprehension (143.50), and overall helpfulness (171.75).
• Automatic evaluation metrics such as METEOR (20.04), BLEU@4 (30.06), and ROUGE-L (20.44) further affirm its advanced capability in generating human-like reviews.

Implications and Future Directions

The research presents significant implications for the future of scientific writing and peer review processes. The system’s ability to produce feedback that sometimes surpasses human-generated reviews opens pathways for integrating AI-generated critiques into conventional practices. This could streamline the peer review process, reduce reviewer workload, and enhance the overall quality of scientific discourse.

The paper also prompts future developments in AI-driven feedback systems. One such avenue could be the refinement of literature retrieval mechanisms to minimize biases and improve the accuracy of related work critiques. Moreover, enhancing the efficiency of the system and expanding its accessibility would be critical for broader adoption.

Conclusion

The paper presents a robust and well-validated approach to automated focused feedback generation in scientific writing through SWIF$^{2}$T. By advancing beyond surface-level improvements, it offers deep, actionable, and contextually enriched feedback, highlighting the potential of AI in augmenting academic writing and peer reviewing processes. This work sets a foundational precedent for further exploration and integration of automated systems in scholarly communication.