LLM Document Chunk Generation With Hallucination Evaluation
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Solution Overview
Problem
The creation of textual documents, such as statements of work, is tedious and prone to errors due to the need for knowledge and experience to ensure coherence and avoidance of confusing or impermissible terms, especially when defining the goals and obligations of involved parties.
Innovation Solution
A method using a large language model to generate and evaluate document chunks based on topical information, with prompts and context, and an evaluation module to identify and address hallucinations, ensuring coherence and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a large language model generates document chunks independently, then generation speed increases, but coherence and consistency between chunks deteriorate
Solution Approach 1:
The document generation process is divided into independent chunks that can be generated in parallel, with each chunk processed separately by the large language model while maintaining overall document coherence through structured prompting and evaluation
Solution Approach 2:
An evaluation module assesses generated chunks for hallucinations and coherence, providing feedback to identify and correct inconsistencies, ensuring the final document maintains stability and coherence across all generated sections
2Reliability
If manual review and editing of document chunks is performed, then document accuracy improves, but time consumption increases
Solution Approach 1:
The evaluation module automatically detects and flags hallucinations in generated chunks without requiring manual review, enabling the system to self-correct errors through automated feedback mechanisms, thus maintaining high accuracy while minimizing time loss
Solution Approach 2:
Automated evaluation provides immediate feedback on generated chunks, identifying hallucinations and inconsistencies that can be corrected programmatically, replacing time-consuming manual review with efficient automated quality assurance
3Reliability
If comprehensive evaluation for hallucinations is performed, then document reliability improves, but processing complexity increases
Solution Approach 1:
The evaluation module acts as an intermediary between the generation and final assembly processes, systematically identifying and flagging hallucinations without requiring complex manual intervention, thus improving reliability while managing processing complexity through automated mediation
Data Source
AI summary
A method of generating a document having multiple chunks of text that collectively form the document is disclosed herein that includes receiving topical information; dependent upon the topical information, determining a first chunk of text to generate; retrieving at least one example first chunk of text; providing the at least one example first chunk of text and at least a portion of the topical information to a first large language model; prompting, by a computer processor, the first large language model to generate the first chunk of text through the use of a first request that includes a prompt that states a desired purpose of the first chunk of text to be generated, a context that provides information dependent upon the topical information, and the at least one example first chuck of text; and generating the first chunk of text dependent upon the topical information.