Benchmarking Dataset Generation for Accurate Document Summarization
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Solution Overview
Problem
Existing document management platforms and large language models are ineffective in accurately analyzing and summarizing electronic documents, lacking mechanisms to assess the effectiveness of these models, leading to inaccurate or incorrect results.
Innovation Solution
A system is developed to generate benchmarking datasets using labeled and unlabeled electronic documents, processed by generative AI models, to create validated labels that are used to train and evaluate large language models, reducing computational resources and enhancing accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large language models are used to process and summarize electronic documents, then document analysis capability is improved, but computational resources and time consumption increase significantly
Solution Approach 1:
The system performs preliminary actions by generating summaries and extracting labels from documents before the actual model evaluation process. Benchmarking datasets are prepared in advance with ground truth labels, allowing the large language model to work with pre-processed information rather than raw documents, thus reducing computational resources during the main analysis task.
Solution Approach 2:
The document processing task is segmented into distinct components: summary generation, label extraction, and benchmarking evaluation. By dividing the complex document analysis into these manageable segments and evaluating them separately using benchmarking datasets, the system reduces the computational burden on the large language model while maintaining analysis accuracy.
2Productivity
If existing large language models are used for document summarization, then processing speed is improved, but result accuracy deteriorates due to model ineffectiveness
Solution Approach 1:
The system implements feedback mechanisms by creating benchmarking datasets with ground truth labels that allow evaluation of model performance. This feedback loop enables identification of ineffective models and guides selection or improvement of models that maintain both processing speed and summarization accuracy, resolving the contradiction between productivity and reliability.
3Loss of information
If comprehensive document analysis is performed without benchmarking, then analysis thoroughness is improved, but lack of effectiveness assessment leads to undetected errors
Solution Approach 1:
The system introduces benchmarking datasets as an intermediary between document analysis and effectiveness assessment. These datasets serve as a mediator that enables precise measurement of model performance without compromising the thoroughness of document analysis. The benchmarking process provides a structured way to assess effectiveness while maintaining analysis completeness.
Data Source
AI summary
A method, a system, and a computer program product for generating a benchmarking dataset. One or more queries for generation of one or more summaries of one or more electronic documents are received. The queries are modified using one or more parameters associated with the electronic documents to generate modified queries. The electronic documents are sent to a generative artificial intelligence (AI) model. The generative AI model generates summaries of the electronic documents based on at least one of: the initial queries and the modified queries. One or more labels for the electronic documents are generated using the summaries.