XtraGPT: Context-Aware and Controllable Academic Paper Revision via Human-AI Collaboration

• URL: http://arxiv.org/abs/2505.11336v2
• Date: Mon, 04 Aug 2025 14:42:02 GMT
• Title: XtraGPT: Context-Aware and Controllable Academic Paper Revision via Human-AI Collaboration
• Authors: Nuo Chen, Andre Lin HuiKai, Jiaying Wu, Junyi Hou, Zining Zhang, Qian Wang, Xidong Wang, Bingsheng He,
• Abstract summary: XtraGPT is the first suite of open-source large language models (LLMs) designed to provide context-aware, instruction-guided writing assistance.<n>We introduce a dataset of 7,040 research papers from top-tier venues annotated with over 140,000 instruction-response pairs.<n>Experiments validate that XtraGPT significantly outperforms same-scale baselines and approaches the quality of proprietary systems.
• Score: 41.44785777328187
• 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 when it comes to supporting 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. We first introduce a comprehensive dataset of 7,040 research papers from top-tier venues annotated with over 140,000 instruction-response pairs that reflect realistic, section-level scientific revisions. Building on the dataset, we develop XtraGPT, the first suite of open-source LLMs, designed to provide context-aware, instruction-guided writing assistance, ranging from 1.5B to 14B parameters. 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 our models in improving scientific drafts.

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