Embedding-Space Text Updating for Accurate Item Descriptions
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Solution Overview
Problem
Large language models generate verbal content that may include hallucinations, errors, malicious content, data privacy issues, misinformation, and bias, and lack functionality to provide meaningful suggestions and updates based on relevant or recent data.
Innovation Solution
A system that utilizes a large language model to generate multiple semantic representations of a textual description, embeds these representations in an embedding space, compares them with a database of known texts, and updates the description to include relevant semantic elements, thereby adapting content to user objectives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If large language models are used to generate textual content, then writing efficiency is improved, but content accuracy and reliability deteriorate due to hallucinations and errors
Solution Approach 1:
The system generates multiple semantic representations of the input text, embeds them in a vector space, and uses these embeddings to retrieve and incorporate relevant external information. This feedback loop allows the model to correct its own outputs by comparing generated content against verified external knowledge sources, thereby improving accuracy while maintaining generation efficiency
Solution Approach 2:
The patent introduces an intermediary embedding space and retrieval mechanism that mediates between the LLM's generation process and external knowledge. The embedding layer acts as a bridge, transforming text into vector representations that can be efficiently searched and compared, allowing the system to incorporate accurate external information without compromising the LLM's efficient generation capability
2Adaptability or versatility
If training data for LLMs is updated frequently, then content relevance is improved, but computational resources and time required for retraining increase
Solution Approach 1:
The system segments the knowledge base into discrete external data sources that can be independently queried, rather than requiring complete retraining of the LLM. The embedding space is also segmented into regions representing different knowledge domains, allowing selective retrieval of relevant information based on the specific query, thereby reducing computational overhead while maintaining relevance
Solution Approach 2:
The system performs preliminary embedding of external knowledge data into the vector space in advance, so that when new information becomes available, it can be quickly integrated by updating the embedding space rather than retraining the entire model. This preliminary preparation allows the system to adapt to new information with minimal computational resources
3Quantity of substance
If LLMs are trained on comprehensive data, then knowledge coverage is improved, but inability to access recent data decreases adaptability
Solution Approach 1:
The system creates a dynamic knowledge access mechanism where the embedding space can be continuously updated with new external data without requiring LLM retraining. The retrieval component dynamically queries the embedding space for recent and relevant information based on the specific input, allowing the system to adapt to current events and trends while maintaining the comprehensive knowledge base from original training data
Data Source
AI summary
Systems and methods are disclosed herein for generating updated descriptions of items based on analyzing candidate embeddings of semantic representations of item descriptions. The system may obtain a text file describing an item. The system may provide the text file to a generative language model to generate semantic representations of the text file. The system may generate, based on the text file, candidate embeddings in an embedding space. The system may obtain embeddings associated with existing items. The system may determine subsets of the embeddings within a threshold distance. The system may compare the subsets. The system may determine attributes associated with a candidate embedding based on the comparison. The system may generate an updated text file based on the attributes.