Agent Response Generation Using Content Sketches And Structure Constraints
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Solution Overview
Problem
Existing systems for generating responses in computer interactions are limited by pre-planned messages, often leading to incorrect or inadequate responses.
Innovation Solution
A system that generates responses using structure-based constraints, incorporating conversational context, external world state, and current operations data to create a content sketch, which is then used to generate relevant instructions and responses through machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If pre-planned messages are used to respond to customer queries, then the system operation is simplified and fast, but the response accuracy and relevance deteriorate
Solution Approach 1:
The system performs preliminary actions by generating a content sketch that outlines the reasoning steps and structure before producing the final response. This preliminary planning phase enables the model to organize its thoughts and select appropriate information, thereby improving response accuracy while maintaining operational efficiency.
Solution Approach 2:
The content sketch acts as an intermediary between the customer query and the final response. It serves as a structured representation of the reasoning process, allowing the system to translate complex multi-step reasoning into accurate and relevant responses without sacrificing response speed.
2Device complexity
If the response scope is limited by pre-planned messages, then the system complexity is reduced, but the adaptability to different customer queries deteriorates
Solution Approach 1:
The system dynamically adjusts its response generation process by creating content sketches tailored to each specific customer query. This dynamic approach allows the system to maintain a manageable level of complexity while adapting its reasoning and response scope to match the specific needs of each query, thereby improving versatility without excessive complexity.
Solution Approach 2:
The response generation process is segmented into distinct phases: understanding the query, generating a content sketch with reasoning steps, and producing the final response. This segmentation allows the system to handle complex adaptability requirements in a structured manner, breaking down the complex task of generating appropriate responses into manageable stages.
3Reliability
If multi-step generation is used to reason and plan responses, then the response quality improves, but the time required for processing increases
Solution Approach 1:
The system performs preliminary reasoning and planning by generating a content sketch that captures the essential steps and structure of the response before final generation. This preliminary action allows multi-step reasoning to be conducted efficiently, improving response quality while minimizing the time penalty by organizing thoughts in advance.
Solution Approach 2:
The system skips unnecessary intermediate steps by directly generating the content sketch that contains the essential reasoning structure. This approach allows the system to perform multi-step generation efficiently by focusing on the critical reasoning steps while bypassing redundant processing, thereby reducing processing time while maintaining response quality.
Data Source
AI summary
A system generates a response to a change of state by an automated agent using structure-based constraints. The system receives input regarding an interaction with a client, external data, and current operations data. The received input is used to generate a content sketch. The content sketch can include a plan for how to generate the response. The program language can be constrained to a set of known atoms, such as for example particular specified function, values, and flow control. Relevant instructions for generating a response are generated from the content sketch and an instruction bank. The relevant instructions are then used to generate a response. The generated response may then be executed by the automated agent system.