LLM Path Flagging for Undesirable Output Control
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Solution Overview
Problem
Large language models (LLMs) can generate undesirable outputs due to unintentional bias, inaccurate information, and drift over time, which existing mechanisms struggle to effectively limit.
Innovation Solution
The method involves detecting undesirable LLM outputs, identifying the path used to generate the output, and performing remedial actions such as modifying parameters or flagging paths to modify how the path affects future outputs without retraining the LLM.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If LLM is retrained to eliminate undesirable output, then output quality improves, but time consumption and computational resources increase significantly
Solution Approach 1:
The patent segments the LLM into multiple independent paths (first path, second path, etc.) that can be individually identified, flagged, and modified. Instead of retraining the entire model, only specific paths producing undesirable output are targeted for modification through parameter adjustments or flagging, dramatically reducing time and computational resource requirements while maintaining output quality improvements.
2Reliability
If LLM parameters are modified to limit undesirable output, then output reliability improves, but model adaptability may deteriorate
Solution Approach 1:
The patent applies local quality by modifying parameters or flagging only specific paths within the LLM that produce undesirable output, rather than applying uniform changes across the entire model. This localized approach allows the model to maintain its overall adaptability and versatility while improving reliability in specific problematic areas through path-specific parameter adjustments.
Solution Approach 2:
The patent implements dynamic path flagging where paths are flagged based on real-time detection of undesirable output patterns. The system can dynamically identify when a flagged path is being used and apply validation or parameter modifications only when needed, allowing the model to adapt its behavior dynamically without permanently sacrificing overall adaptability.
3Reliability
If path flagging and validation is implemented, then output quality control improves, but system complexity increases
Solution Approach 1:
The patent extracts the path identification and flagging functionality as a separate mechanism from the main LLM processing. The system identifies specific paths, flags them independently, and applies validation only when flagged paths are detected. This extraction reduces system complexity by creating a modular approach where path management is separated from core model operations.
Solution Approach 2:
The patent introduces an intermediary validation mechanism that sits between path identification and output generation. When a flagged path is detected, the intermediary validation step is triggered to assess whether the output meets quality standards before final generation. This intermediary layer provides quality control without requiring complete system redesign.
4Reliability
If multiple paths are monitored and flagged, then ability to limit undesirable output improves, but computational overhead increases
Solution Approach 1:
The patent applies partial action by monitoring and flagging only the specific paths that produce undesirable output, rather than continuously validating all possible model paths. The system flags paths based on detected patterns and applies validation selectively when flagged paths are identified, reducing computational overhead while maintaining effective limitation of undesirable output.
Data Source
AI summary
Limiting undesired large language model (LLM) output, including: detecting that an output of a large language model (LLM) satisfies one or more conditions indicating that the output is undesirable; identifying a path in the large language model used to generate the output; and performing, based on the path, one or more remedial actions to modify how the path affects output by the LLM.