IntellectSeeker: A Personalized Literature Management System with the Probabilistic Model and Large Language Model
Weizhen Bian, Siyan Liu, Yubo Zhou, Dezhi Chen, Yijie Liao, Zhenzhen, Fan, Aobo Wang

TL;DR
IntellectSeeker is a personalized literature management platform that combines a probabilistic filtering model with a Large Language Model to improve academic search accuracy, relevance, and user experience, especially for newcomers.
Contribution
It introduces a novel integration of LLM-based semantic enhancement with a probabilistic filtering model for personalized academic literature search.
Findings
Enhanced search precision and relevance for users.
Effective bridging of general language and academic terminology.
Improved user experience with summaries and visualizations.
Abstract
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…
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Taxonomy
TopicsSemantic Web and Ontologies · Biomedical Text Mining and Ontologies · Topic Modeling
MethodsRefunds@Expedia|||How do I get a full refund from Expedia? · {Dispute@FaQ-s}How to file a dispute with Expedia? · Attention Is All You Need · Attention Dropout · Softmax · Cosine Annealing · Byte Pair Encoding · Linear Layer · Linear Warmup With Cosine Annealing · Multi-Head Attention
