Dialogue Context Control for LLM Task-Oriented Process Flows
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing AI systems lack effective methods for task-oriented dialogue management that can handle complex user interactions, including deviations from scripted conversations and ensure adherence to predefined computational logic.
Innovation Solution
A system and method for task-oriented dialogue with in-context learning, utilizing a large language model (LLM) to interpret user inputs within the context of defined computing process flows, enabling dialogue understanding and automatic conversation repair.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional NLU systems are used to interpret user messages in isolation, then the system can process individual messages, but it cannot understand the greater context of back-and-forth conversations
Solution Approach 1:
The system transitions from processing single messages in isolation to processing multi-turn dialogue sequences by adding the temporal dimension of conversation history. The LLM receives the current user message along with the full dialogue context, enabling it to understand references, pronouns, and intent that depend on previous turns.
Solution Approach 2:
The system pre-processes and structures the dialogue history before presenting it to the LLM. Conversation turns are organized with speaker labels and timestamps, and the context is prepared in advance to guide the LLM's interpretation of the current message within the proper conversational framework.
2Adaptability or versatility
If fixed intent lists are used in NLU systems, then the system has clear classification boundaries, but it cannot generate new intents or adapt to user goals dynamically
Solution Approach 1:
The system changes the fundamental parameter of intent representation from fixed categorical labels to generated command sequences. Instead of mapping user messages to predetermined intents, the LLM generates natural language commands that reflect the user's actual goals, allowing dynamic adaptation while maintaining reliability through the structured command format.
Solution Approach 2:
The LLM autonomously determines what commands to generate based on the dialogue context and user message, without requiring pre-defined intent categories. The system serves itself by having the model generate both the interpretation and the appropriate command sequence, adapting to new user goals as they emerge in conversation.
3Ease of operation
If LLM generates free-form text output, then the system has high flexibility in response, but it risks hallucination and increased latency
Solution Approach 1:
The system changes the output parameter from free-form text generation to constrained command sequence generation. The LLM is prompted to output specific command formats (e.g., 'collect', 'inform', 'confirm') rather than free-form text, which reduces hallucination while maintaining the flexibility to handle diverse user requests through the rich command language.
4Adaptability or versatility
If comprehensive dialogue understanding is implemented, then the system can handle complex user interactions, but it increases token generation cost and latency
Solution Approach 1:
The system segments the dialogue understanding task into distinct components: context analysis, intent recognition, command generation, and flow management. The LLM focuses on generating commands rather than producing full responses, dividing the computational work between the model and the deterministic flow execution engine, thereby reducing token generation requirements and latency.
Data Source
Figure 1A
Figure 1B
Figure 2
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media for computing process flow control via determination of dialogue context between a user and an artificial intelligence assistant. Multiple defined computing process flows are received by the system. Each of the defined computing process flows describe one or more instructions to be performed. Information about the defined computing process flows and dialogue between the user and the artificial intelligence assistant are provided as a prompt to a large language model (LLM). The system receives from the LLM one or more selected commands for a defined computing process flow relating to the context of the received input from the user. The system performs the command as to the computing process flow.