Sparse Feature Control for Desired LLM Output Generation
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Solution Overview
Problem
Existing large language models (LLM) struggle to generate desired output information without a suitable change policy for generating appropriate advertisements, leading to inefficiencies in content delivery.
Innovation Solution
An information processing apparatus that estimates a change policy for a learning model using sparse feature values to adjust the output of an LLM, determining correction values to enhance the generation of desired output information, such as advertisements, by converting feature values from an intermediate layer and applying transformation matrices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a large language model generates output information directly without policy adjustment, then the generation process is simple and fast, but the suitability and relevance of the generated content (especially advertisements) cannot be ensured
Solution Approach 1:
The patent introduces sparse feature values as an intermediary between the LLM's feature output and the final generation policy. These sparse feature values act as a mediator that extracts and represents key semantic information, enabling policy estimation without directly modifying the complex LLM architecture. This resolves the contradiction by adding a lightweight intermediate layer that improves reliability without proportionally increasing complexity.
Solution Approach 2:
The patent changes the parameter representation from dense feature values to sparse feature values, and further to discrete policy representations. By transforming the feature space from continuous high-dimensional vectors to sparse discrete policies, the system achieves better controllability and suitability while managing complexity through dimensionality reduction and discretization.
2Adaptability or versatility
If sparse feature values are used to estimate change policy, then the generation of desired output information becomes possible, but additional processing steps and computational resources are required
Solution Approach 1:
The patent extracts only the essential and relevant features from the LLM's output by converting dense feature values to sparse feature values. This extraction process removes redundant information and retains only the critical elements needed for policy estimation, thereby improving adaptability while actually reducing the computational burden compared to processing all original features.
Solution Approach 2:
The patent applies different processing qualities to different parts of the feature space. Instead of uniformly processing all feature dimensions, it identifies and processes only the locally relevant features that contribute to generation policy, achieving high adaptability where needed while maintaining efficiency in less critical areas.
3Loss of information
If feature values from intermediate layers are converted to sparse feature values, then generation policy can be estimated, but the conversion process adds computational overhead
Solution Approach 1:
The patent creates a simplified copy or representation of the essential information from dense feature values in the form of sparse feature values. Rather than processing the full high-dimensional feature space, it creates a compressed symbolic representation that preserves the critical generation policy information while requiring significantly less computational power for subsequent processing.
Data Source
AI summary
An information processing apparatus according to the present application includes an estimation unit and a determination unit. The estimation unit estimates, for a learning model learned to generate, as output information, an answer to a question input as input information, based on a sparse feature value obtained by converting a feature value output by a predetermined layer of the learning model when predetermined input information is input, the sparse feature value indicating a generation policy for generating output information corresponding to the predetermined input information by the learning model, a change policy for the learning model to generate desired output information. The determination unit determines, based on the sparse feature value and the change policy, a correction value for changing the sparse feature value.