用大模型和概率模型帮学者精准找文献，省时又懂行。
IntellectSeeker: A Personalized Literature Management System with the Probabilistic Model and Large Language Model
- 用大模型把日常语言转成学术术语，新手也能准确提问。
- 基于用户行为和需求，概率模型过滤文献，推荐更贴合兴趣。
- 支持智能推荐与摘要压缩，适合科研新手和高效研究者。
面对学术文献的爆炸式增长，研究人员常因文章质量不确定和关键词搜索不匹配而困扰。我们提出 IntellectSeeker——一个融合大语言模型（LLM）与概率模型的个性化智能文献管理平台。该平台采用 GPT-3.5-turbo 模型，通过多轮少样本学习，将日常语言转换为各场景下的专业学术术语，显著帮助学术新手跨越术语鸿沟。其概率模型基于用户显性需求与行为模式，智能筛选与个人兴趣高度契合的学术文章。此外，系统集成先进推荐机制与文本压缩工具，依据用户互动实现智能推荐，并以简洁的一行摘要和创新词云可视化呈现结果，大幅提升研究效率与使用体验。IntellectSeeker 为学术研究者提供高度定制化的文献管理方案，具备出色的搜索精度与匹配能力。
原文摘要 · Abstract (English)
Faced with the burgeoning volume of academic literature, researchers often need help with uncertain article quality and mismatches in term searches using traditional academic engines. We introduce IntellectSeeker, an innovative and personalized intelligent academic literature management platform to address these challenges. This platform integrates a Large Language Model (LLM)--based semantic enhancement bot with a sophisticated probability model to personalize and streamline literature searches. We adopted the GPT-3.5-turbo model to transform everyday language into professional academic terms across various scenarios using multiple rounds of few-shot learning. This adaptation mainly benefits academic newcomers, effectively bridging the gap between general inquiries and academic terminology. The probabilistic model intelligently filters academic articles to align closely with the specific interests of users, which are derived from explicit needs and behavioral patterns. Moreover, IntellectSeeker incorporates an advanced recommendation system and text compression tools. These features enable intelligent article recommendations based on user interactions and present search results through concise one-line summaries and innovative word cloud visualizations, significantly enhancing research efficiency and user experience. IntellectSeeker offers academic researchers a highly customizable literature management solution with exceptional search precision and matching capabilities. The code can be found here: https://github.com/LuckyBian/ISY5001
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