Topic-Directed Review Summaries for LLM Prompt Limits
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Solution Overview
Problem
Off-the-shelf generative language models (LLMs) struggle with summarizing large volumes of diverse user reviews for products or services, often omitting important details, including irrelevant information, and failing to focus on relevant topics, making it difficult for users to understand the quality of the generated summaries.
Innovation Solution
The method involves associating reviews with relevant topics through clustering processes or using topic classifiers, generating input prompts that include selected reviews based on these topics, and instructing LLMs to generate summaries focused on those topics, thereby improving the quality and relevance of the generated summaries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If off-the-shelf LLMs are used to summarize large amounts of reviews, then the summarization task can be automated, but the model may select content at random, omit important details, or include irrelevant information
Solution Approach 1:
The patent segments the summarization task into multiple stages: (1) topic identification from reviews, (2) review selection based on topic relevance, and (3) summary generation. This segmentation allows each stage to be optimized independently, improving overall summary accuracy while maintaining automation.
Solution Approach 2:
The patent performs preliminary actions before the main summarization task by first identifying relevant topics from the reviews and pre-selecting reviews that are most relevant to those topics. This preliminary filtering ensures that the LLM receives curated input, reducing random content selection and improving summary precision.
2Loss of information
If all reviews are input to the LLM for summarization, then comprehensive coverage is achieved, but the input prompt limit caused by position embedding matrix size restricts the number of reviews that can be processed
Solution Approach 1:
The patent extracts only the most relevant reviews for each identified topic before inputting them to the LLM. By taking out and selecting only the essential reviews rather than processing all reviews, the system overcomes the input prompt limit while maintaining comprehensive coverage of important information.
Solution Approach 2:
The patent applies partial action by processing a subset of reviews (those most relevant to identified topics) rather than all reviews. This selective approach allows the system to work within LLM input constraints while still achieving effective summarization of the most important review content.
3Measurement precision
If additional instructions or context are provided to the LLM to improve summary quality, then the relevance of the summary improves, but the complexity of the input prompt and processing increases
Solution Approach 1:
The patent performs preliminary topic identification and review selection before generating the input prompt for the LLM. By preparing structured topic information and pre-selected reviews in advance, the system provides the LLM with clear context and instructions without creating excessive prompt complexity during the actual summarization step.
Data Source
AI summary
Products or services may be reviewed by previous users, and an LLM may be to generate a summary review summarizing these reviews. However, LLMs may have difficulty summarizing large amounts of diverse text, and additional instructions or context in an input prompt may guide summarization of these reviews by the LLM. A computer-implemented method may involve: associating reviews with topics; and generating an input prompt for an LLM comprising selected reviews of the reviews and instructing generation of a summary review of the selected reviews. The selected reviews may be selected from amongst the reviews based on the topics associated with the reviews. The method may further involve inputting the input prompt into the LLM and obtaining the summary review as generated by the LLM.