Towards Adaptive Context Management for Intelligent Conversational Question Answering

2025-09-22

AI Summary

Conversational question answering (CQA) faces challenges in efficiently leveraging lengthy dialogue histories due to model token-length constraints. To address this, we propose an adaptive context management framework comprising three synergistic components: dynamic context pruning, sliding-window-based summarization, and key entity extraction—enabling hierarchical compression of dialogue history while preserving semantically critical information. Unlike static truncation or global summarization, our method dynamically selects and refines relevant historical segments based on the current query’s semantics, maximizing contextual relevance and coherence within the token budget. Experiments on multi-turn QA benchmarks demonstrate significant improvements in response accuracy and contextual coherence, alongside enhanced robustness and long-range dependency modeling. The framework establishes a scalable new paradigm for dialogue state management in resource-constrained settings.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Natural Language Processing: Summarization
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior

Application Category

• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Community question answering

Abstract

This particular paper introduces an Adaptive Context Management (ACM) framework for the Conversational Question Answering (ConvQA) systems. The key objective of the ACM framework is to optimize the use of the conversation history by dynamically managing context for maximizing the relevant information provided to a ConvQA model within its token limit. Our approach incorporates a Context Manager (CM) Module, a Summarization (SM) Module, and an Entity Extraction (EE) Module in a bid to handle the conversation history efficaciously. The CM Module dynamically adjusts the context size, thereby preserving the most relevant and recent information within a model's token limit. The SM Module summarizes the older parts of the conversation history via a sliding window. When the summarization window exceeds its limit, the EE Module identifies and retains key entities from the oldest conversation turns. Experimental results demonstrate the effectiveness of our envisaged framework in generating accurate and contextually appropriate responses, thereby highlighting the potential of the ACM framework to enhance the robustness and scalability of the ConvQA systems.

Problem

• Optimizing conversation history usage in question answering systems
• Managing context dynamically within token limit constraints
• Preserving relevant information while handling lengthy conversations

Innovation

• Adaptive Context Management framework for Conversational Question Answering
• Dynamically adjusts context size to preserve relevant information
• Summarizes history and retains key entities via modules