Interdependent Document Chunk Generation With Hallucination Checks
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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 relevance, and existing systems struggle with generating consistent and error-free documents.
Innovation Solution
A method using a large language model to generate and evaluate document chunks based on topical information, ensuring consistency and accuracy by using an index to retrieve relevant text chunks and employing a second model to detect hallucinations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual document creation is performed, then coherence and relevance can be ensured through knowledge and experience, but the process is tedious and time-consuming
Solution Approach 1:
The document is divided into multiple chunks that can be generated independently and then assembled. Each chunk is generated by retrieving relevant information from an index and using a large language model, allowing parallel processing and reducing overall time consumption while maintaining coherence through the assembly process
Solution Approach 2:
An information index is introduced as an intermediary between the user's topical information and the document generation process. The index stores and organizes relevant information, allowing the system to efficiently retrieve and utilize knowledge without requiring manual documentation of all relevant details
2Reliability
If manual document creation is performed, then coherence and relevance can be ensured through knowledge and experience, but errors occur due to lack of proper knowledge and information
Solution Approach 1:
Instead of requiring human experts to create documents from scratch, the system copies and adapts relevant information from pre-existing examples stored in the index. The large language model generates new document chunks by learning from and copying patterns from these examples, reducing the need for human expertise while maintaining accuracy
Solution Approach 2:
The system incorporates a feedback mechanism where generated chunks are evaluated for consistency and accuracy. The large language model can review and refine its own output, and the assembly process includes checks to ensure coherence, allowing errors to be detected and corrected automatically
3Productivity
If document chunks are generated independently, then generation speed increases, but consistency between chunks deteriorates
Solution Approach 1:
The system performs preliminary actions by retrieving relevant information from the index before generating each chunk. This ensures that each chunk is based on consistent, pre-verified information, and the large language model uses context from previously generated chunks to maintain consistency throughout the document
Solution Approach 2:
The assembly process includes feedback mechanisms that review generated chunks for consistency with each other and with the overall document structure. The system can identify and correct inconsistencies, ensuring that independently generated chunks maintain coherence when assembled
Data Source
AI summary
A method of generating a document having multiple chunks of text that collectively form the document is disclosed herein that can include determining a first chunk of text to generate dependent upon topical information relevant to the document that is to be created and retrieving at least one example first chunk of text dependent upon a desired purpose of the first chunk and upon the topical information. The method can further include generating the first chunk of text by a first large language model via a first request. The method can also include determining a second chunk of text to generate dependent upon the topical information and retrieving at least one example second chunk of text dependent upon a desired purpose of the second chunk and upon the topical information. Additional steps can include generating the second chunk of text by the first large language model via a second request.