Test Cases for Training LLMs

When training Large Language Models (LLMs), particularly for tasks like **extracting information from tax documents**, writing effective test cases is crucial for ensuring your model learns as intended and can accurately perform the desired function. These test cases differ significantly from traditional software testing due to the probabilistic and generative nature of LLMs.

I. Data Quality and Coverage Tests

These tests focus on the training data itself.

• Vocabulary Coverage

  • Positive Case: Ensure key tax-related terms and entities are present (e.g., “Adjusted Gross Income,” “Taxable Income,” “Form 1040,” “W-2,” dates, amounts).
  • Negative Case: Verify absence of irrelevant or harmful content.
  • Edge Case: Check for less common tax forms or specific legal jargon.

• Data Distribution and Balance

  • Positive Case: Ensure a reasonable balance across different tax forms and scenarios (e.g., different income levels, filing statuses).
  • Negative Case: Identify potential biases due to overrepresentation or underrepresentation of certain tax situations.

• Data Format and Consistency

  • Positive Case: Verify consistent formatting of tax document text and annotations (if used for training).
  • Negative Case: Identify and handle inconsistencies in data structure or formatting.

• Noise and Error Rate

  • Positive Case: Training data contains clean and accurate text extracted from tax documents.
  • Negative Case: Identify and potentially filter out data points with significant OCR errors or inconsistencies.

• Contextual Completeness

  • Positive Case: Training data provides sufficient context within the tax documents for the model to accurately identify and extract specific information.
  • Negative Case: Identify examples where the context is insufficient or ambiguous.

II. Model Behavior and Output Tests

These tests evaluate the LLM‘s performance after training.

• Accuracy and Relevance

  • Positive Case: Given a tax document, the model accurately extracts the requested information (e.g., “What is the Taxable Income?”).
  • Negative Case: Ambiguous queries should not lead to incorrect extractions or hallucinations.
  • Edge Case: Test with variations in how information is presented across different tax forms.

• Fluency and Coherence (If Generating Explanations)

  • Positive Case: If the model is also generating explanations, the output should be grammatically correct and coherent.
  • Negative Case: Identify nonsensical or incorrect explanations.

• Bias and Fairness

  • Negative Case: Test for potential biases in information extraction related to demographic data (if present and relevant, though usually not the primary focus of tax document extraction).

• Robustness and Adversarial Attacks (Simplified)

  • Negative Case: Test sensitivity to minor variations in the input text (e.g., extra spaces, slight OCR errors) that shouldn’t change the extracted information.

• Hallucinations and Factual Accuracy

  • Positive Case: The model only extracts information present in the document and does not invent values or details.
  • Negative Case: Identify instances where the model hallucinates information not present in the tax document.

• Instruction Following

  • Positive Case: The model accurately follows instructions regarding the type of information to extract or the format of the output.
  • Negative Case: Test if instructions are ignored or misinterpreted.

• Efficiency and Resource Usage

  (Less Direct Test Cases) Monitor training time and resource use.

III. Retrieval-Augmented Generation (RAG) Specific Tests

These tests are specific to RAG implementations.

• Relevance of Retrieved Documents

  • Positive Case: When asked a question about a specific tax concept, the retrieved documents (e.g., IRS guidelines, explanations) are relevant.
  • Negative Case: Identify irrelevant or outdated tax information being retrieved.

• Accuracy of Information in Retrieved Documents

  • Positive Case: Information in retrieved tax documents and guidelines is accurate and up-to-date.
  • Negative Case: Identify errors or inconsistencies in retrieved data.

• Integration of Retrieved Information

  • Positive Case: The LLM effectively uses retrieved information to answer complex tax-related questions based on the provided documents.
  • Negative Case: LLM ignores or misinterprets retrieved data.

• Handling of “Not Found” Scenarios

  • Positive Case: When information is not present in the provided tax documents or the knowledge base, the model provides an appropriate “not found” or “cannot answer” response.

IV. Test Case Design Principles

• Focus on Key Functionality (Information extraction)
• Variety and Coverage (Different tax forms, data layouts)
• Clear Expected Outcomes (Specific values or pieces of information)
• Automation (Consider using testing frameworks – unittest, pytest)
• Human Evaluation (Especially for complex extractions or explanations)
• Iterative Refinement

V. Example Test Cases (Tax Document Information Extraction)

Test Case ID	Input Prompt	Expected Output	Category
TAX_POS_001	From this W-2, what is the Federal Income Tax withheld?	$XXXX.XX
TAX_NEG_001	What is the capital of France?	I am designed to extract information from tax documents.
TAX_EDGE_001	What is the total income reported on this Form 1040, line 9?	$YYYY.YY
TAX_NEG_002	According to this document, what was my stock portfolio value on January 1, 2024?	Information not found in this document.	Hallucinations
TAX_RAG_POS_001	Explain the requirements for claiming the Earned Income Tax Credit.	(Explanation based on retrieved IRS guidelines)	RAG Integration
TAX_RAG_NEG_001	Retrieve information about gardening tips.	(No relevant tax-related documents retrieved)	RAG Relevance
TAX_POS_002	From the provided Schedule A, what is the total amount of itemized deductions?	$ZZZZ.ZZ
TAX_POS_003	What is the filing status indicated on this Form 1040?	Single / Married filing jointly / etc.

VI. Tools and Techniques

• Manual Testing (Crucial for verifying complex extractions)
• Automated Testing Frameworks: unittest, pytest
• Evaluation Metrics (e.g., Exact Match for extracted values, F1 score for more complex extractions)
• Logging and Monitoring

By implementing these test case strategies, you can improve the reliability and performance of your LLM for tax document information extraction.