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

TL;DR

KScope is a hierarchical framework that systematically characterizes the knowledge status of large language models using statistical tests, improving understanding and updating of model knowledge across various models and datasets.

Contribution

The paper introduces a taxonomy of knowledge statuses and a hierarchical framework, KScope, for detailed analysis and characterization of LLM knowledge states.

Findings

Context narrows knowledge gaps across models.

Features like difficulty and relevance influence knowledge updates.

Models show similar preferences when partially correct or conflicted, but differ when wrong.

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.