XtraGPT: Context-Aware and Controllable Academic Paper Revision

• URL: http://arxiv.org/abs/2505.11336v3
• Date: Thu, 23 Oct 2025 14:49:19 GMT
• Title: XtraGPT: Context-Aware and Controllable Academic Paper Revision
• Authors: Nuo Chen, Andre Lin HuiKai, Jiaying Wu, Junyi Hou, Zining Zhang, Qian Wang, Xidong Wang, Bingsheng He,
• Abstract summary: We propose a human-AI collaboration framework for academic paper revision centered on criteria-guided intent alignment and context-aware modeling.<n>We instantiate the framework in XtraGPT, the first suite of open-source LLMs for context-aware, instruction-guided writing assistance.
• Score: 43.263488839387584
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite the growing adoption of large language models (LLMs) in academic workflows, their capabilities remain limited to support high-quality scientific writing. Most existing systems are designed for general-purpose scientific text generation and fail to meet the sophisticated demands of research communication beyond surface-level polishing, such as conceptual coherence across sections. Furthermore, academic writing is inherently iterative and revision-driven, a process not well supported by direct prompting-based paradigms. To address these scenarios, we propose a human-AI collaboration framework for academic paper revision centered on criteria-guided intent alignment and context-aware modeling. To validate the framework, we curate a dataset of 7,000 research papers from top-tier venues annotated with 140,000 instruction-response pairs that reflect realistic, section-level scientific revisions. We instantiate the framework in XtraGPT, the first suite of open-source LLMs (1.5B to 14B parameters) for context-aware, instruction-guided writing assistance. Extensive experiments validate that XtraGPT significantly outperforms same-scale baselines and approaches the quality of proprietary systems. Both automated preference assessments and human evaluations confirm the effectiveness of XtraGPT in improving scientific drafts.

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