LLM Hallucination Detection Based on PDF Retrieval: A RAG-Enhanced Reliability Solution

This project explores the use of PDF document retrieval combined with Retrieval-Augmented Generation (RAG) technology to detect and mitigate hallucination issues in large language models (LLMs). By verifying model outputs against real documents, it provides a traceable and interpretable solution for the reliability of LLM-generated content.

LLMs have achieved remarkable results in natural language processing, but they face the hallucination problem—generating content that is inconsistent with facts, unsubstantiated, or self-contradictory, which may lead to serious consequences in high-precision fields such as healthcare and law. The root cause is that LLMs are probabilistic generators that rely on statistical patterns in training data and lack the ability to understand and verify facts.

Retrieval-Augmented Generation (RAG) guides model generation by introducing external knowledge retrieval. Its advantages include: traceability (information sources are verifiable), timeliness (knowledge bases are easy to update), domain adaptability (professional knowledge bases improve accuracy), and hallucination detection capability (comparing consistency between outputs and documents).

PDF is chosen as the knowledge source due to its practicality (used in formal documents like academic papers and legal texts), but it faces technical challenges such as document parsing (extracting text/tables, etc.), semantic chunking (balancing granularity), vectorization (building embedding models and vector databases), and retrieval strategies (algorithm selection and reordering).

The detection workflow includes: 1. Query generation (constructing retrieval queries from key claims extracted from LLM outputs); 2. Document retrieval (obtaining relevant fragments from the PDF knowledge base); 3. Consistency comparison (checking whether outputs are supported or contradicted by documents); 4. Hallucination determination (marking potential hallucinations without evidence); 5. Feedback mechanism (prompting users or triggering re-generation).

This solution is applicable to scenarios such as academic research assistance (verifying the accuracy of literature reviews), legal document analysis (ensuring correct citation of provisions/case law), medical information verification (filtering incorrect medical advice), and financial report generation (verifying that financial analysis aligns with original documents).

Limitations include insufficient knowledge base coverage, risk of retrieval failure, limitations of comparison algorithms, and high computational costs. Relevant technical trends include multi-modal RAG, active retrieval, self-reflection mechanisms, and adversarial training.

Hallucination detection based on PDF retrieval is an important direction to improve LLM reliability. Although there are technical challenges, with the maturity of RAG technology and the improvement of knowledge bases, it is expected to make LLM-generated content more credible and usable in practical applications.