Improved Evidence Extraction and Metrics for Document Inconsistency Detection with LLMs
Revolutionizing Document Inconsistency Detection with Advanced LLM Strategies
This paper addresses limitations in LLM-based document inconsistency detection by proposing new evidence-extraction metrics and a 'redact-and-retry' framework with constrained filtering. The approach significantly improves performance over other prompting methods and is supported by strong experimental results. A new semi-synthetic dataset, ContraDocPaired, is introduced to evaluate evidence extraction with evidence sets of size two, enhancing diversity beyond previous datasets.
Key Impact Metrics
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EPRC (Evidence Precision Rate When Correct)
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ERRC (Evidence Recall Rate When Correct)
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Average # Retries
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Methodology Overview
Evidence Extraction Metrics
Dataset Contribution
The paper introduces a novel 'redact-and-retry' framework, which, when combined with a constrained filtering mechanism (RnR+CF), significantly enhances LLM performance in evidence extraction for document inconsistency detection.
Enterprise Process Flow
Input Document (x_i)
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LLM Initial Classification (y_i^(1), E_i^(1))
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Check if y_i^(j) = Yes
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Redact (x_i^(j-1), E_i^(j-1))
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LLM Re-evaluation (y_i^(j), E_i^(j))
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End Loop if y_i^(j) = No
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Aggregate Evidence (E_i = Union of E_i^(j))
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Apply Constrained Filter
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EPRC (Evidence Precision Rate When Correct) for RnR+CF, indicating strong precision in identifying inconsistent sentences when the LLM's classification is correct.
Beyond simple evidence hit rates, the paper introduces comprehensive metrics like Evidence Precision Rate (EPR), Evidence Recall Rate (ERR), and their 'when correct' counterparts (EPRC, ERRC) to holistically evaluate LLM's evidence extraction capabilities, penalizing both over- and under-extraction.
| Metric | Description | RnR+CF Advantage |
|---|---|---|
| EHR | Strict metric requiring entire evidence set to be identified. |
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| EPR | Penalizes large evidence sets with false positives. |
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| ERR | Lenient metric allowing partial evidence extraction. |
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The paper introduces ContraDocPaired, a semi-synthetic dataset based on ContraDoc, featuring documents with two inconsistent sentences. This addresses a limitation of previous datasets which only had single inconsistent sentences, allowing for more diverse evaluation of evidence extraction.
ContraDocPaired: Addressing Dataset Limitations
The original ContraDoc dataset had a limitation: each positive document contained exactly one inconsistent sentence. This restricted the evaluation of LLM evidence extraction to single-sentence scenarios. To overcome this, the authors created ContraDocPaired, a semi-synthetic dataset where each positive document contains two inconsistent sentences. This was achieved by combining two original positive datapoints from ContraDoc, using an optimal-greedy algorithm to minimize new datapoint length.
Key Results:
- Enables more diverse evaluation of evidence extraction abilities, especially for multi-sentence inconsistencies.
- Addresses the challenge of detecting widely separated inconsistencies in longer documents.
- Contributes a valuable resource for future research in document inconsistency detection with LLMs.
Calculate Your Potential ROI
Estimate the significant time and cost savings your enterprise could achieve by automating document inconsistency detection.
Estimated Annual Savings
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Annual Hours Reclaimed
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Your Enterprise AI Roadmap
A structured approach to integrating LLM-powered inconsistency detection into your operations.
Phase 1: Initial Model Integration
Integrate the base LLM inconsistency detection with existing document processing pipelines. Establish baseline performance metrics.
Phase 2: Redact-and-Retry Customization
Implement and fine-tune the 'redact-and-retry' framework, adapting redaction logic and retry conditions to specific enterprise document structures.
Phase 3: Constrained Filtering Deployment
Deploy the constrained filtering mechanism, carefully configuring thresholds and ensuring optimal balance between precision and recall for extracted evidence. Monitor false positive/negative rates.
Phase 4: Continuous Learning & Refinement
Establish feedback loops for continuous model improvement, leveraging new data and user-reported inconsistencies to refine LLM prompts and filtering rules.
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