Sparse Feature Control for Policy-Guided LLM Output
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Solution Overview
Problem
Existing large language models (LLM) have limited application ranges, making it difficult to generate suitable output information based on predetermined change policies.
Innovation Solution
An information processing apparatus that includes a change unit to modify sparse feature values output by a learning model, allowing it to generate desired output information by converting these values based on a predetermined policy, and a generation unit to utilize these modified values for generating appropriate responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a predetermined change policy is applied to modify sparse feature values, then the ability to generate suitable output information based on specific policies is improved, but the device complexity increases due to the additional change unit and feature value conversion processes
Solution Approach 1:
The patent introduces a change unit as an intermediary component that modifies sparse feature values before they are used by the learning model. This mediator translates predetermined change policies into actionable feature value adjustments, enabling policy-based control without directly modifying the learning model structure. The change unit acts as a bridge between the policy layer and the model layer, resolving the contradiction by adding functional complexity only where needed.
Solution Approach 2:
The patent modifies the sparse feature values (parameters) of the learning model dynamically based on predetermined change policies. Instead of changing the model architecture or adding complex control systems, the invention achieves adaptability by adjusting the feature value parameters before they are fed into the model. This parameter-based approach enables flexible policy implementation with minimal structural complexity.
2Manufacturing precision
If sparse feature values are converted and modified before input to the learning model, then the precision and control of output information generation is improved, but the processing time and computational overhead increase
Solution Approach 1:
The patent performs feature value conversion and modification as a preliminary action before the learning model generates output information. By pre-processing the sparse feature values according to predetermined policies, the system ensures that the model receives already-adjusted inputs, reducing the need for post-processing corrections and improving overall processing efficiency. This preliminary adjustment stage enables precise control without significantly increasing total processing time.
3Adaptability or versatility
If the learning model uses converted sparse feature values to generate output information, then the applicability range of the model is improved, but the loss of original feature information may occur during conversion
Solution Approach 1:
The patent creates a copied and modified version of the sparse feature values rather than directly altering the original features. The change unit generates transformed feature values that preserve the essential information while incorporating predetermined policy changes. This copying approach allows the model to utilize both the original feature structure and the policy-based modifications, minimizing information loss while expanding applicability.
Data Source
AI summary
An information processing apparatus according to the present application includes a change unit and a generation unit. The change unit changes, based on a predetermined change policy, a sparse feature value obtained by converting a feature value output by a predetermined layer in a learning model when predetermined input information is input to the learning model learned to generate, as output information, an answer to a question input as input information, the sparse feature value indicating a generation policy for the learning model to generate output information corresponding to the predetermined input information. The generation unit causes the learning model to generate output information corresponding to the predetermined input information using a feature value obtained by converting the changed sparse feature value changed by the change unit as a feature value output by the predetermined layer in the learning model.