ScholarCopilot-v1: AI for Academic Writing

ScholarCopilot-v1, developed by TIGER-Lab, is a 7.6 billion parameter language model foundational to the Scholar Copilot system. This model is uniquely trained on the entire Arxiv full paper corpus, enabling it to seamlessly integrate automatic text completion and intelligent citation suggestions into the academic writing process. It functions as a unified model for retrieval and generation, adept at making context-sensitive decisions regarding when and what to cite, and how to generate coherent content based on reference papers.

Key Capabilities

• Next-3-Sentence Suggestions: Predicts subsequent sentences with automatic retrieval and citation of relevant reference papers.
• Citation Suggestions on Demand: Provides precise, contextually appropriate paper citations.
• Full Section Auto-Completion: Assists in brainstorming and drafting comprehensive paper content and structure, particularly for introduction and related work sections.

Good For

• Researchers and academics seeking to enhance productivity and creativity in writing.
• Generating high-quality text and precise citation recommendations within academic papers.
• Integrating an AI-driven pipeline for iterative and context-aware Retrieval-Augmented Generation (RAG) in academic contexts.