[Paper Review] AcademicGPT: Empowering Academic Research

Shufa Wei, Xiaolong Xu+22 more|arXiv (Cornell University)|Nov 21, 2023

Topic Modeling4 citations

TL;DR

AcademicGPT is a domain-specific large language model fine-tuned from LLaMA2-70B on a 120-billion-token academic corpus, including research papers, theses, and high-quality Chinese academic content. It achieves strong performance on general (MMLU, CEval), Chinese (CEval), and specialized academic benchmarks (PubMedQA, SCIEval, ComputerScienceQA), enabling advanced applications like AI-assisted paper reading, review, title/abstract generation, and multi-turn academic question answering.

ABSTRACT

Large Language Models (LLMs) have demonstrated exceptional capabilities across various natural language processing tasks. Yet, many of these advanced LLMs are tailored for broad, general-purpose applications. In this technical report, we introduce AcademicGPT, designed specifically to empower academic research. AcademicGPT is a continual training model derived from LLaMA2-70B. Our training corpus mainly consists of academic papers, thesis, content from some academic domain, high-quality Chinese data and others. While it may not be extensive in data scale, AcademicGPT marks our initial venture into a domain-specific GPT tailored for research area. We evaluate AcademicGPT on several established public benchmarks such as MMLU and CEval, as well as on some specialized academic benchmarks like PubMedQA, SCIEval, and our newly-created ComputerScienceQA, to demonstrate its ability from general knowledge ability, to Chinese ability, and to academic ability. Building upon AcademicGPT's foundation model, we also developed several applications catered to the academic area, including General Academic Question Answering, AI-assisted Paper Reading, Paper Review, and AI-assisted Title and Abstract Generation.

Motivation & Objective

• Address the challenge of information overload in academic research by developing a specialized LLM tailored for scientific literature.
• Overcome the limitations of general-purpose LLMs in understanding complex academic nuances, especially in technical and domain-specific contexts.
• Enable efficient, accurate, and context-aware academic workflows through AI-powered tools for reading, reviewing, and content generation.
• Demonstrate the effectiveness of continual pretraining on curated academic corpora to enhance domain-specific reasoning and knowledge retention.
• Bridge the gap between advanced LLM capabilities and real-world academic research needs through practical, application-driven deployment.

Proposed method

• Continual pretraining of LLaMA2-70B on a 120-billion-token corpus comprising academic papers, theses, domain-specific content, and high-quality Chinese academic data.
• Leveraging the ReAct framework to build a multi-turn, context-aware General Academic Question Answering agent with memory and reasoning capabilities.
• Designing a supervised fine-tuning pipeline using 1 million labeled research papers to train generation models for title and abstract prediction.
• Formulating input data as "Introduction Experiments Results <begin_generate>Title:content;Abstract:content" to guide conditional generation.
• Developing four core applications: General Academic Q&A, AI-assisted Paper Reading, AI-based Paper Review, and AI-assisted Title/Abstract Generation.
• Employing special tokens like <begin_generate> to trigger and control the generation process in a structured, reliable manner.

Experimental results

Research questions

• RQ1 Can a continual pretraining strategy on a curated academic corpus significantly improve a LLM’s performance on academic reasoning and knowledge benchmarks?
• RQ2 To what extent can a domain-specific LLM like AcademicGPT outperform general-purpose LLMs in understanding and generating scientific content?
• RQ3 How effective are AI-assisted tools built on AcademicGPT in supporting real academic workflows such as paper reading, review, and content generation?
• RQ4 Can the ReAct framework be effectively adapted to maintain context and reasoning quality in multi-turn academic dialogues?
• RQ5 What is the impact of data formulation and fine-tuning on the coherence and relevance of generated academic titles and abstracts?

Key findings

• AcademicGPT achieves strong performance on MMLU and CEval, demonstrating robust general knowledge and reasoning capabilities.
• On specialized benchmarks like PubMedQA and SCIEval, AcademicGPT shows significant capability in biomedical and scientific reasoning tasks.
• The newly created ComputerScienceQA benchmark confirms AcademicGPT’s strong performance in computer science-specific knowledge and problem-solving.
• The AI-assisted paper reading system effectively summarizes complex academic content, highlighting key findings and improving comprehension.
• The AI-based paper review system provides critical, context-aware feedback, enhancing the quality and clarity of academic writing.
• The title and abstract generation system produces coherent, relevant, and accurate outputs, with minimal accuracy loss in evaluation, as shown in case studies with real research papers.