View Proposal

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Proposer

Zi Hau Chin

Title

Automated Detection of Hallucinated and Unsupported Citations

Goal

Design and Evaluation of a PDF-Based System for Detecting Hallucinated and Incorrect Citations in Documents

Description

To design, implement, and evaluate a system that accepts academic papers in PDF format, automatically extracts in-text citations and reference-list entries, verifies cited publications using multiple scholarly metadata sources, identifies bibliographic inconsistencies, and presents explainable results within an interactive PDF viewer. -Corresponds to a real publication? -Has accurate authors, title, year, venue, volume, pages, and DOI? -Combines information from several unrelated publications? -Has been attached to a claim that the source does not support? The system should: -Process text-based academic PDFs -Detect the reference section -Extract at least two major citation styles -Link in-text citations to references -Verify citations through at least two scholarly sources -Flag major metadata inconsistencies -Highlight the citation location in the PDF -Export an explainable report -Outperform a simple DOI-only or exact-match baseline

Resources

VeraCite, Verify Citation, BibGuard, CheckIfExist

Background

Python programming and text processing, REST APIs and structured data formats such as JSON, Natural language processing and semantic similarity , PDF processing and information extraction

Url

Difficulty Level

High

Ethical Approval

Full

Number Of Students

1

Supervisor

Zi Hau Chin

Keywords

citation hallucination, reference verification, bibliographic entity matching, doi validation, semantic similarity, natural language inference

Degrees

Bachelor of Science in Computing Science