LLM Subject Line Generation via Keyword Extraction
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Solution Overview
Problem
Current machine learning models struggle to predictably generate summaries from a body of text, requiring multiple iterations and significant processing power, especially when aiming for a specific aspect or tone in the summary.
Innovation Solution
A two-step approach using a machine learning model to extract keywords from a body of text, which are then used to prompt a large language model (LLM) to generate a subject line, reducing the need for the LLM to process the full body of text.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the full body of text is included in the prompt to the LLM, then the LLM can generate more accurate and contextually appropriate subject lines, but the processing power and computational resources required increase significantly
Solution Approach 1:
The patent extracts only the essential information (keywords) from the body of text and uses these extracted keywords in the prompt to the LLM, rather than including the full text. This extraction approach maintains the necessary contextual information for accurate subject line generation while significantly reducing the amount of data processed, thereby lowering computational resource consumption.
Solution Approach 2:
The patent segments the text processing task into two distinct stages: first, extracting keywords from the body text using a summarizer model; second, using these keywords to generate subject lines using the LLM. This segmentation allows each model to focus on its specific function, optimizing both accuracy and computational efficiency.
2Manufacturing precision
If multiple iterations are performed to achieve a desired summary with specific tone or aspect, then the summary quality improves, but the time required and processing power consumed increase
Solution Approach 1:
The patent performs preliminary keyword extraction from the body text before prompting the LLM to generate subject lines. This preliminary action prepares the essential information in advance, allowing the LLM to generate high-quality subject lines in fewer iterations without needing to reprocess the full text each time, thereby reducing overall time and computational cost.
3Reliability
If the LLM processes the full body of text, then the model can avoid hallucinations and maintain accuracy, but the prompt becomes longer and processing power requirements increase
Solution Approach 1:
The patent extracts only the necessary keywords from the body text and uses these condensed keywords in the LLM prompt instead of the full text. This extraction maintains the essential information needed to prevent hallucinations while significantly reducing prompt length and avoiding the processing overhead of analyzing entire documents.
Data Source
AI summary
Methods and systems for prompting a large language model (LLM) to generate a subject line for a body of text are disclosed. An original list of keywords based on a body of text is obtained using a trained machine learning model. A prompt to the LLM is then generated for generating the subject line, where the prompt includes a chosen list of keywords that is based on the original list of keywords. The prompt does not include the body of text. Responsive to the prompt, at least one generated subject line corresponding to the body of text is obtained from the LLM.