Natural Language Query Reformulation Using Crowdsourced Feedback
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Solution Overview
Problem
Users interacting with natural language interface (NLI) systems often struggle to phrase their queries effectively, leading to sub-optimal and time-consuming interactions due to ambiguity in question phrasing, which can result in irrelevant or unsatisfactory answers.
Innovation Solution
Implementing crowdsourced reinforcement learning to optimize NLI systems by analyzing patterns from prior user interactions, adjusting queries to align with user intent, and providing refined answers through an NLI process that includes a user interaction analyzer, interaction registry, and a language model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users phrase queries in natural language without optimization, then the interface is easy to use, but the query accuracy and relevance to user intent deteriorate
Solution Approach 1:
The system introduces an intermediary component (query optimization module) that translates between natural language queries and optimized query formulations. This intermediary analyzes the user's intent and reformulates the query to improve accuracy while maintaining ease of use, effectively mediating between the user's simple input and the system's need for precise information retrieval.
Solution Approach 2:
The system implements feedback mechanisms where query performance data is collected and used to continuously improve query optimization strategies. By analyzing successful query patterns and user interactions, the system refines its optimization approach over time, improving query accuracy without increasing user effort.
2Measurement precision
If iterative query refinement is allowed, then query accuracy can be improved, but the time required to obtain satisfactory answers increases
Solution Approach 1:
The system performs preliminary actions by proactively optimizing queries before they are executed. Instead of waiting for iterative user refinement, the system pre-processes and optimizes the query based on available context and historical data, delivering accurate results in fewer interaction steps and reducing overall time consumption.
Solution Approach 2:
The query optimization process is dynamic and adaptive, adjusting its strategy based on real-time context, user preferences, and system state. This dynamic approach allows the system to efficiently balance between query accuracy and response time by selecting the most appropriate optimization level for each specific query scenario.
3Adaptability or versatility
If ambiguous terms are allowed in queries, then the interface remains simple and flexible, but the reliability of results deteriorates
Solution Approach 1:
The system changes parameters by dynamically adjusting query formulation based on detected ambiguity. When ambiguous terms are identified, the system modifies query parameters such as adding contextual filters, specifying time ranges, or incorporating additional criteria to disambiguate the intent, thereby maintaining flexibility while improving result reliability.
Solution Approach 2:
The system applies preliminary anti-action by proactively preventing ambiguity from causing unreliable results. Through intent analysis and context evaluation, the system anticipates potential ambiguity issues and counteracts them by reformulating the query or seeking clarification before executing the search, ensuring reliable results while preserving interface simplicity.
Data Source
AI summary
In one implementation, a device receives a query from a user for input to a large language model. The device matches a pattern associated with the query with one or more prior chat exchanges between the large language model and one or more other users. The device generates an adjusted query based on the query and the one or more prior chat exchanges. The device provides an answer to the adjusted query from the large language model to the user.