Improved Evidence Extraction and Metrics for Document Inconsistency Detection with LLMs
Summary: arXiv:2601.02627v2 Announce Type: replace-cross
Abstract: Large language models (LLMs) are becoming useful in many domains due to their impressive abilities that arise from large training datasets and large model sizes. However, research on LLM-based approaches to document inconsistency detection is relatively limited. We address this gap by investigating evidence extraction capabilities of LLMs for document inconsistency detection. To this end, we introduce new comprehensive evidence-extraction metrics and a redact-and-retry framework with constrained filtering that substantially improves evidence extraction performance over other prompting methods. We support our approach with strong experimental results and release a new semi-synthetic dataset for evaluating evidence extraction.
Introduction
The rapid evolution of artificial intelligence has led to the emergence of large language models (LLMs), which have shown significant promise in various applications. One critical area of application is document consistency detection, where LLMs can assist in identifying discrepancies within texts. This article discusses recent advancements in evidence extraction methodologies tailored for LLMs and outlines a framework designed to enhance performance in detecting document inconsistencies.
Research Gaps in Document Inconsistency Detection
Despite the growing capabilities of LLMs, research specifically focusing on their application to document inconsistency detection remains sparse. Traditional methods often fall short in efficiently extracting relevant evidence from documents. Our study aims to bridge this gap by exploring how LLMs can be leveraged more effectively for this purpose.
Methodology
To enhance evidence extraction capabilities, we propose the following:
- New Evidence-Extraction Metrics: We introduce a set of metrics that provide a comprehensive evaluation of how well LLMs can extract pertinent evidence related to inconsistencies found in documents.
- Redact-and-Retry Framework: This innovative framework employs a constrained filtering mechanism that allows for iterative refinement of the evidence extraction process, leading to significantly improved outcomes.
Experimental Results
We conducted extensive experiments to validate our approach. The results indicate that our new metrics and framework outperform existing methods in terms of precision and recall in evidence extraction tasks. The findings highlight the effectiveness of using LLMs for identifying inconsistencies in documents, showcasing their potential for enhancing document analysis.
New Semi-Synthetic Dataset
In conjunction with our research, we have developed and released a new semi-synthetic dataset. This dataset serves as a benchmark for evaluating evidence extraction methodologies and provides a robust platform for future research in this domain. By sharing this resource, we aim to foster collaboration and innovation among researchers focused on document inconsistency detection.
Conclusion
The integration of large language models in the field of document inconsistency detection represents a significant advancement. Our research demonstrates that by refining evidence extraction techniques and introducing innovative frameworks, we can enhance the reliability and accuracy of document analysis. As the capabilities of LLMs continue to evolve, we anticipate further breakthroughs that will refine this process, ultimately leading to more reliable documentation practices across various industries.
Future Directions
As we look ahead, several avenues for further exploration emerge:
- Enhancing the robustness of evidence extraction through advanced machine learning techniques.
- Exploring the applicability of our methods across different languages and document types.
- Investigating how these approaches can be integrated into existing document management systems.
In conclusion, the advancements presented in this study pave the way for more sophisticated and efficient document inconsistency detection methodologies that leverage the power of large language models.