LLM File Identifier Retrieval for Accurate Image Output
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Solution Overview
Problem
Large language models (LLMs) are limited in generating non-textual data, leading to reduced interpretability and high hallucination rates, and hybrid models with image reconstruction/generation models consume significant processing resources.
Innovation Solution
Enhanced LLMs generate file resource identifiers for non-textual data, such as images, by retrieving and converting file identifiers into resource identifiers, reducing the need for pixel-by-pixel reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multi-modal LLMs generate images directly, then non-textual output capability is improved, but hallucination rate increases and accuracy decreases
Solution Approach 1:
The patent introduces an intermediary system consisting of an image encoder and image database between the LLM and final image output. The LLM generates text descriptions which are encoded into embeddings, matched against stored image embeddings, and retrieve actual images from the database. This intermediary mechanism prevents the LLM from hallucinating images while maintaining the capability to output non-textual data.
Solution Approach 2:
The patent creates a copy of the image database with pre-encoded embeddings that serves as a reference for the LLM. Instead of generating images from scratch, the system copies and retrieves existing images that match the LLM's text descriptions, ensuring accuracy while maintaining versatility.
2Measurement precision
If hybrid models with image reconstruction models are used, then image generation accuracy is improved, but processing resource consumption increases significantly
Solution Approach 1:
The patent performs preliminary encoding of all images in the database before they are needed for generation. Image embeddings are pre-computed and stored in the database, so when the LLM needs to generate an image, the system only needs to retrieve and match embeddings rather than perform complex image reconstruction computations in real-time.
Solution Approach 2:
The patent extracts the computationally intensive image generation task from the LLM and separates it into a distinct retrieval and matching process. The LLM only generates text descriptions, while the image retrieval system handles the actual image selection and delivery, removing the heavy computational burden from the language model.
3Productivity
If LLMs output only text-based responses, then processing efficiency is maintained, but interpretability and user understanding decrease
Solution Approach 1:
The patent segments the output generation process into distinct components: the LLM generates text descriptions, the image retrieval system processes and delivers images, and the final output combines both. This segmentation allows the LLM to maintain its text-based efficiency while the separate image module adds visual interpretability without interfering with the LLM's processing speed.
Data Source
AI summary
Techniques for enhancing language model capabilities are disclosed herein. An example computer-implemented method comprises receiving an input prompt comprising textual data and generating output data by a large language model (LLM) based at least in part on the input prompt. The output data includes a file identifier associated with a file stored in a storage location. The example computer-implemented method further comprises retrieving, based on the file identifier, a file resource identifier that indicates the storage location and replacing the file identifier within the output data into the file resource identifier. The example computer-implemented method further comprises causing the output data to be displayed to a user, which includes causing an image associated with the file resource identifier to be displayed.