Specialized LLM Token Prediction for Human Intervention
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Solution Overview
Problem
Existing large language models (LLMs) lack the ability to recognize when external intervention, such as human assistance, is necessary to adequately respond to task-oriented prompts, leading to the generation of incorrect and misleading responses.
Innovation Solution
Training LLMs to predict specialized tokens, including action, speaker, and time tokens, to determine when external intervention is required, pausing response generation and requesting human assistance as needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If LLM generates text-based responses automatically without external intervention, then response speed is improved, but response accuracy and reliability deteriorate for task-oriented prompts
Solution Approach 1:
The LLM predicts action tokens in advance to identify when external intervention is needed before generating the final response. This preliminary detection mechanism allows the system to pause and seek external assistance proactively, ensuring task completion before response generation, thereby maintaining both speed and reliability.
Solution Approach 2:
The system implements a feedback loop where the LLM's predicted action tokens trigger external intervention signals. The external system processes the task and feeds back the completion status to the LLM, which then generates the final response. This feedback mechanism ensures accuracy while maintaining overall response efficiency.
2Reliability
If LLM pauses to seek external intervention for every task, then response accuracy is improved, but response time increases
Solution Approach 1:
The LLM uses partial action by predicting action tokens selectively rather than pausing for every possible task. The model identifies specific scenarios where external intervention is critically needed based on its training, seeking assistance only when necessary. This partial intervention approach maintains accuracy for critical tasks while minimizing unnecessary delays for routine operations.
3Adaptability or versatility
If LLM is trained with specialized action tokens, then ability to recognize when intervention is needed is improved, but model complexity increases
Solution Approach 1:
The action token mechanism serves multiple functions: it identifies when external intervention is needed, communicates this need to external systems, and guides the response generation process. This single multi-functional mechanism enhances adaptability without requiring separate complex subsystems for each function, thereby limiting the increase in overall model complexity.
Data Source
AI summary
Certain aspects of the disclosure provide methods for training and using large language models (LLMs) to predict specialized tokens for prompting human intervention. A method generally includes obtaining a plurality of training data instances, each including: a training input comprising a first timestamp and a prompt and/or an intermediate response to the prompt; and a training output comprising a second timestamp and a response; annotating the training output of one or more training data instances to include an action token indicating that external intervention is required, wherein the annotation, for each training data instance, is based on: a time difference between the second timestamp and the first timestamp; a number of words included in the response; or at least one trigger word included in the response; and training the LLM on the training data instances to predict when external intervention is required and accordingly generate the action token.