Conversational Relevance Modeling for Noisy External Context
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Solution Overview
Problem
Conversational systems based on large language models face challenges in managing excessive external context and noise, which affect data integrity and relevance, making it difficult to provide accurate and context-aware responses.
Innovation Solution
A method for context-aware relevancy modeling that selects latent static content and external context using a scoring function to generate a relevant response to user queries, incorporating a fusion and relevance function to optimize context selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If external context data is extensively collected and associated with user queries, then the system can provide more comprehensive context-aware responses, but the data management becomes exhaustive and complex
Solution Approach 1:
The patent segments the external context data into multiple dimensions (e.g., user profile, conversation history, domain-specific knowledge) and processes each dimension separately through dedicated processing modules. This segmentation reduces the complexity of managing the entire context dataset while maintaining comprehensive context-awareness through integrated processing of segmented components.
Solution Approach 2:
The patent introduces intermediary components such as context embedding layers and attention mechanisms that mediate between the raw external context data and the query processing. These intermediaries transform complex context data into manageable representations, reducing the direct complexity of data management while preserving the adaptability for context-aware responses.
2Loss of information
If extensive external context data is processed, then more relevant information can be provided, but noise increases and affects data integrity
Solution Approach 1:
The patent applies local quality by differentiating the processing and weighting of different context data sources based on their relevance and reliability. High-quality, low-noise context sources receive higher weights and more detailed processing, while noisier sources are filtered or down-weighted. This selective quality approach maintains information completeness while reducing noise interference.
Solution Approach 2:
The patent implements feedback mechanisms through attention scores and relevance weighting that continuously evaluate the quality and reliability of external context data. Based on this feedback, the system dynamically adjusts the incorporation of different context sources, reducing noise interference while maintaining comprehensive information utilization.
Data Source
AI summary
A method, computer system, and a computer program product are provided for a context-aware relevancy modelling in conversational systems. A user query is received. A latent static content d is selected from a corpus of content D. A latent set of context C from a set of external context Cu is also selected. A result is generated using a scoring function and using the latent static content d from a corpus D and the latent set of context C from the set of external contexts CU so as to provide a most relevant context-base search response to said user query q. The result provides a most relevant context-base search response to said user query q. A response is then generated based on said result using said scoring function result to said user query q.