Expert Dialogue Management for Semantics-Aware RL Response Selection
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Solution Overview
Problem
Large language models struggle with generating dialogue responses that consider conversation history semantics and are computationally expensive for processing and retraining.
Innovation Solution
A computing system that processes conversation data with a language encoding model and multiple expert language models to generate candidate utterances, using a dialogue management model trained with reinforcement learning to select a predicted response, reducing the need for retraining billions of parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If large language models are used to generate dialogue responses, then the model can process general language tasks, but it fails to properly generate responses with consideration of conversation history semantics and is computationally expensive
Solution Approach 1:
The patent segments the dialogue management task into multiple specialized expert models, each trained on specific dialogue skills or topics. Instead of using a single large language model for all tasks, the system divides the workload among smaller expert models (e.g., greeting expert, information seeking expert, closing expert), reducing the computational burden while maintaining response quality for specific dialogue scenarios.
Solution Approach 2:
The patent applies local quality by training each expert model on specific dialogue skills or domains rather than using a general-purpose model. Each expert model develops specialized knowledge for its specific function (e.g., one expert handles greetings, another handles information seeking), improving the quality of responses for particular dialogue types without requiring the entire system to be universally competent.
2Adaptability or versatility
If large language models with billions of parameters are used, then the model has extensive language knowledge, but it requires computationally expensive processing and retraining
Solution Approach 1:
The system segments the large language model into multiple smaller expert models, each handling specific dialogue skills. This segmentation reduces the number of parameters that need to be processed and retrained while maintaining comprehensive language knowledge coverage across different dialogue scenarios through the collective expertise of multiple specialized models.
Solution Approach 2:
The patent creates a universal dialogue management system where multiple expert models can handle various dialogue tasks. Each expert model is specialized for a particular function, but collectively they provide universal coverage for different dialogue scenarios, achieving versatility without requiring a single large model to handle everything.
3Device complexity
If a single large language model is used for dialogue management, then the model structure is simple, but it struggles with dialogue management and carrying on rich engaging conversations
Solution Approach 1:
The patent segments the dialogue management system into multiple expert models, each responsible for specific dialogue skills. This segmentation increases structural complexity but dramatically improves dialogue management capability by allowing each expert to specialize in particular conversational patterns, leading to more engaging and contextually appropriate responses.
Solution Approach 2:
The system implements dynamic expert selection where the appropriate expert model is chosen based on the current dialogue context and state. This dynamic approach allows the system to adapt its complexity to the specific task at hand, using only the necessary expert knowledge for each interaction rather than relying on a static, overly simple model structure.
Data Source
AI summary
Systems and methods for dialogue response prediction can leverage a plurality of machine-learned language models to generate a plurality of candidate outputs, which can be processed by a dialogue management model to determine a predicted dialogue response. The plurality of machine-learned language models can include a plurality of experts trained on different intents, emotions, and/or tasks. The particular candidate output selected may be selected by the dialogue management model based on semantics determined based on a language representation. The language representation can be a representation generated by processing the conversation history of a conversation to determine conversation semantics.