How can I trigger human review for low-confidence PDF data extraction using LLMs?

Below is an approach that can help you trigger human review without doubling your costs:

1. Leverage Built-In Confidence Scoring

• Prompt Engineering for Self-Assessment:
  Instead of running two separate LLMs, consider prompt-engineering your chosen LLM (whether GPT-4 or Gemini) to output both the extracted data and an associated confidence score. For example, you can ask the model to “explain its level of certainty” about each field it extracts. Tailoring your prompt can get the model to provide a qualitative or quantitative score.

• Schema Validation:
  Use domain-specific rules (e.g., validating formats for invoice numbers, dates, or monetary amounts) as post-processing checks. If the extracted value violates these rules or falls outside expected ranges, lower confidence can be inferred.

2. Integrate an Automated Validator

• Secondary Lightweight Evaluator:
  Instead of deploying two full LLM setups, you can build a lightweight rule-based or ML system to cross-check the outputs against known patterns. This system would flag anomalies or discrepancies in the extracted data as triggers for human review.

• Post-Processing with an LLM Evaluator:
  Another approach is to have a second LLM instance (perhaps GPT-4 configured to be cost-efficient in this role) that reviews the extraction output. This can be done with a limited token budget by only feeding in specific parts of the output that are critical. The review LLM can compare the data to expected formats or common errors and only escalate when it detects issues.

3. Iterative Refinement and Feedback Loop

• Feedback from Human Review:
  Establish a loop where cases that are flagged (and later verified by humans) are used to further refine the confidence scoring mechanism. Over time, this helps in calibrating the thresholds, reducing the need for human intervention.

• Adaptive Thresholds:
  Adjust the thresholds dynamically—for instance, if the model has been reliable on specific fields over time, you might lower the threshold for those and only trigger reviews for the fields that historically carry higher risk.

Summary

Instead of a multi-agent approach that doubles costs, use one of the following streamlined strategies:

• Enhance your prompts to make the LLM generate self-assessment metrics.
• Apply post-processing validation rules—either via a simple rule-based check or a lightweight LLM evaluator—to identify likely errors.
• Use iterative feedback to refine your extraction system so that human review is only triggered when absolutely necessary.

This approach minimizes duplication of work while still ensuring that low-confidence outputs are flagged for review, thereby balancing performance with cost-efficiency.