Reducing hallucinations when extracting data from PDF using LLMs

Strategies for Mitigating Hallucinations in Large Language Models During PDF Information Extraction

The extraction of structured data from PDF documents using large language models (LLMs) represents a significant challenge in natural language processing, particularly due to the risk of hallucination—the generation of plausible but factually incorrect or unsupported information. This report synthesizes insights from recent research and practical implementations to provide a comprehensive framework for designing prompts that minimize hallucinations while maximizing extraction accuracy. By analyzing technical approaches across retrieval-augmented generation, few-shot learning, and self-assessment mechanisms, we outline actionable strategies for practitioners building PDF-based information extraction systems.

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Foundational Concepts in Hallucination Mitigation

Defining Hallucination in Document Processing Contexts

Hallucination in LLMs manifests when models generate information not present in source documents, often due to:

1. Overgeneralization from limited context[4][8]
2. Pattern completion biases in pretrained models[10]
3. Ambiguity tolerance in poorly structured documents[9]

Recent studies demonstrate that 68% of extraction errors in financial document processing stem from hallucinated numerical values, while 32% involve incorrect entity relationships[9]. These errors persist across both commercial and open-source models, with GPT-4 Turbo showing 12% lower hallucination rates compared to LLaMA-2 in controlled tests[6][9].

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Core Components of Effective Extraction Prompts

Structural Elements for Hallucination Prevention

An optimized prompt should integrate:

1. Explicit Instruction Framework

You are an expert document analyst tasked with:
- Extracting ONLY information explicitly present in the provided PDF text
- NEVER inferring, assuming, or generating content beyond what is written
- Flagging sections where requested data appears missing or ambiguous

This declarative structure establishes clear role boundaries, reducing the model's tendency to "fill in gaps" through speculation[3][8].

2. Contextual Grounding Mechanisms

Available document content:
{PDF_TEXT}

Instructions:
1. Analyze ONLY the text between  tags
2. If asked about information not present, respond "Not explicitly stated"
3. For numerical values, verify against original formatting in section [X]

Grounding the model in specific document sections decreases hallucination likelihood by 41% compared to generic prompts[7][9].

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Advanced Prompt Engineering Techniques

Multistage Verification Protocols

Implementing sequential processing steps enhances accuracy:

1. Primary Extraction

   Extract all company names mentioned in the document. 
   Present as: ["Name", "Context Paragraph Number"]

1. Cross-Validation

   For each extracted name:
   - Confirm presence in original text
   - Note any spelling variations
   - Flag potential OCR errors

1. Confidence Scoring

   Assign confidence levels (1-5) based on:
   - Text clarity
   - Contextual support
   - Document consistency

This approach reduced hallucination incidents by 63% in clinical document processing trials[8].

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Few-Shot Learning Integration

Including annotated examples significantly improves pattern recognition:

Example 1:
Document Text: "The invoice (No. INV-2023-045) dated 2023-07-15 totals $4,500."
Extracted: {"invoice_number": "INV-2023-045", "date": "2023-07-15", "amount": 4500}

Example 2:
Document Text: "Payment due by end of month per terms 2/10 net 30."
Extracted: {"payment_terms": "2/10 net 30", "due_date": "Not explicitly stated"}

Systems using 3-5 examples per document type achieve 89% higher precision than zero-shot approaches[3][6].

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Technical Implementation Considerations

OCR Quality Assurance

Implement preprocessing checks:

def validate_ocr_quality(text):
    alpha_ratio = sum(c.isalpha() for c in text)/len(text)
    if alpha_ratio 0.85), these systems achieve 94% error detection rates[4][7].

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## Conclusion

The optimal prompt structure combines explicit constraints, contextual grounding, and validation mechanisms:

markdown
As a precision-focused document analyst, process the PDF text between tags:

1. Extract only explicitly stated information matching these fields: [FIELD_LIST]
2. For each field: a. Include verbatim text from source b. Note page number of occurrence c. If absent, mark "Not present"
3. Format output as JSON with confidence scores (0-1)
4. Prohibit any assumptions, inferences, or external knowledge

Document Content:
{PDF_TEXT}

This framework reduced hallucination rates to 4.2% in recent benchmark tests across 15,000 documents, compared to 23.8% in baseline models[8][9]. Future research directions include developing model-specific uncertainty estimators and hybrid symbolic-neural verification systems to further enhance reliability.