Author-in-the-Loop Response Generation and Evaluation: Integrating Author Expertise and Intent in Responses to Peer Review
arXiv:2602.11173v3 Announce Type: replace Abstract: Author response (rebuttal) writing is a critical stage of scientific peer review that demands substantial author effort. In practice, authors possess domain expertise, author-only information, and response strategies - concrete forms of author expertise and intent - and seek NLP assistance that integrates these signals into author response generation (ARG). Yet this author-in-the-loop paradigm lacks formal NLP formulation and systematic study: no dataset provides fine-grained author signals, existing ARG work lacks author inputs and controls,
The proliferation of advanced NLP models has created a demand for more refined, human-integrated AI assistance, especially in critical intellectual tasks like peer review.
This development highlights the ongoing evolution of AI from mere automation to sophisticated augmentation, where human expertise guides and refines AI outputs in complex domains.
AI tools for academic writing and review will become more intelligent and context-aware, incorporating author-specific knowledge and strategic intent rather than generating generic responses.
- · Researchers and academics
- · NLP developers focusing on human-in-the-loop systems
- · Scientific publishing platforms
- · Generic AI writing assistants
- · Manual, time-consuming peer review processes
Author response generation tools will significantly improve in quality and utility for domain experts.
This paradigm will likely extend to other professional domains requiring expert-guided AI content generation and review.
The definition of 'authorship' and 'expertise' in an AI-augmented world may subtly shift, emphasizing the crucial role of human guidance in complex intellectual production.
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