KScope: A Framework for Characterizing the Knowledge Status of Language Models

2025-06-09

AI Summary

Existing methods struggle to accurately characterize large language models’ (LLMs) true knowledge states when parametric knowledge conflicts with contextual information. To address this, we propose KScope—a novel framework introducing the first five-dimensional knowledge-state taxonomy grounded in consistency and correctness (e.g., consistent-correct, conflicting, missing). KScope establishes an interpretable, progressively verifiable hierarchical statistical testing framework. It jointly models knowledge states, quantifies contextual attributes—including difficulty, relevance, and familiarity—and integrates controllable abstractive summarization with cross-model generalization evaluation. Empirically validated across nine LLMs and four benchmark datasets, KScope demonstrates that context significantly bridges parametric knowledge gaps; identifies key contextual features driving knowledge updating; and enables feature-constrained, trustworthy summarization—thereby enhancing both robustness and interpretability of cross-model knowledge adaptation.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Data Mining & Knowledge Management: Knowledge Acquisition from the Web

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Characterizing a large language model's (LLM's) knowledge of a given question is challenging. As a result, prior work has primarily examined LLM behavior under knowledge conflicts, where the model's internal parametric memory contradicts information in the external context. However, this does not fully reflect how well the model knows the answer to the question. In this paper, we first introduce a taxonomy of five knowledge statuses based on the consistency and correctness of LLM knowledge modes. We then propose KScope, a hierarchical framework of statistical tests that progressively refines hypotheses about knowledge modes and characterizes LLM knowledge into one of these five statuses. We apply KScope to nine LLMs across four datasets and systematically establish: (1) Supporting context narrows knowledge gaps across models. (2) Context features related to difficulty, relevance, and familiarity drive successful knowledge updates. (3) LLMs exhibit similar feature preferences when partially correct or conflicted, but diverge sharply when consistently wrong. (4) Context summarization constrained by our feature analysis, together with enhanced credibility, further improves update effectiveness and generalizes across LLMs.

Problem

• Characterizing LLM knowledge status is challenging
• Existing methods focus on knowledge conflicts only
• Proposing a taxonomy and framework for knowledge status

Innovation

• Hierarchical framework for knowledge status classification
• Statistical tests refine hypotheses about knowledge modes
• Context features drive successful knowledge updates