One-Shot Query Generation From Multi-Turn Dialogs
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Solution Overview
Problem
Current generative models, such as large language models (LLMs), struggle to handle complex user inputs that require multiple actions and associated parameters, necessitating prolonged dialog interactions and resource utilization, and lack a standard benchmark for one-shot queries.
Innovation Solution
A system processes multi-turn dialogs to generate one-shot queries that accurately reflect user intents, using a generative model to derive and verify query candidates, ensuring they are free from hallucinations and resource-intensive manual curation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If LLMs are trained on extremely large and diverse language datasets to improve generative semantic and compositional power, then the model's natural language processing capability is improved, but the capability to handle complex user input requesting task fulfillment remains insufficient
Solution Approach 1:
The patent segments complex user input handling into two distinct phases: (1) a multi-turn dialog phase where the system interacts with the user to clarify intent and gather necessary information, and (2) a one-shot query execution phase where the refined query is executed. This segmentation allows the LLM to leverage its strong natural language understanding in the dialog phase while achieving reliable task fulfillment in the execution phase.
Solution Approach 2:
The patent performs preliminary actions by generating multiple one-shot query candidates from the multi-turn dialog before selecting the best candidate for execution. This preliminary generation and verification process ensures that the final query accurately reflects user intent, thereby improving both reliability and adaptability for task fulfillment.
2Measurement precision
If conventional NLU and fulfillment systems generate multiple prompts to seek additional user inputs for fulfilling user intent, then the system can gather sufficient information to perform actions, but the interaction becomes time-consuming and low efficiency
Solution Approach 1:
The system performs preliminary actions by generating multiple one-shot query candidates that encapsulate complete user intent in a single query. By preparing these candidates in advance from the multi-turn dialog, the system eliminates the need for multiple sequential prompts during execution, thereby reducing interaction time while maintaining measurement precision of user intent.
Solution Approach 2:
The patent creates multiple copies (candidates) of the one-shot query from the dialog history, each representing a possible interpretation of user intent. These candidate copies are then verified against the dialog context to identify the most accurate representation, enabling efficient single-shot execution without losing intent understanding accuracy.
3Adaptability or versatility
If a standard benchmark for one-shot queries is not available, then existing benchmarks focus on multi-turn dialogs, but this lacks a reference standard for evaluating single complex user input handling
Solution Approach 1:
The patent creates a universal benchmark framework that can evaluate both multi-turn dialog performance and one-shot query performance using the same underlying methodology. The generated one-shot query dataset serves as a standardized reference that can be applied across different evaluation scenarios, providing manufacturing precision for benchmarking while maintaining adaptability to evaluate various dialog types.
4Reliability
If manual curation is used to create training data for complex tasks, then the training data can be accurate, but the process becomes resource-intensive and time-consuming
Solution Approach 1:
The system performs self-service by automatically generating one-shot query candidates from existing multi-turn dialog data without requiring manual annotation for each candidate. The verification mechanism automatically filters and selects high-quality candidates, enabling the system to produce large volumes of accurate training data efficiently, thereby improving productivity while maintaining reliability.
Solution Approach 2:
The verification process provides feedback by evaluating generated one-shot query candidates against the original dialog context and selecting only those that accurately reflect user intent. This feedback loop ensures high accuracy in the generated training data while automating the process to maintain high productivity, eliminating the need for resource-intensive manual curation.
Data Source
AI summary
Implementations relate to processing multi-turn dialogs each showing (1) dialog turns that correspond to user input(s) providing user intent(s) and associated parameter(s), and (2) dialog turns that correspond to input(s) from a virtual assistant (or a human agent/responder) that are responsive to the user input(s). A multi-turn dialog (e.g., a pre-processed variation thereof) can be processed, using a generative model, to generate one or more one-shot queries summarizing the user input(s) of the multi-turn dialog. Whether the generated one-shot queries accurately reflect the user intent(s) and/or the associated parameters can be verified, and only verified one-shot queries are selected to form part of a dataset. The dataset can be used, for example, for training machine learning model(s) for handling a single, complex user query and/or for validating machine learning model(s).