Investigating Reasoning in Large Language Models with Counterfactual Knowledge Graphs
TL;DR
This work delineates LLM reasoning boundaries and presents a new paradigm for fine-grained capability assessment, which suggests that genuine reasoning is demonstrated only when a model follows logical rules despite conflicting prior knowledge.
Abstract
Despite the success of Large Language Models (LLMs) on reasoning benchmarks, it remains unclear whether their performance stems from genuine logical deduction or the memorization of training patterns. Existing benchmarks often fail to disentangle reasoning from prior knowledge, as tasks grounded in real-world facts allow models to take ''knowledge shortcuts''. In this paper, we propose a novel diagnostic benchmark to decouple knowledge memorization from logical reasoning. Built on the DBpedia KG, our framework constructs multi-hop reasoning chains (from Q1 to Q5) across three task dimensions: Factural Questions (FQ), Counterfactual Questions (CQ) with logically consistent but counterfactual conclusions, and Questions with Similar-Entity Options (SO) to evaluate the dependence on prior knowledge. Questions without Context serve only as an intermediate form: they contain solely queries with no triples or options, so LLMs cannot answer them directly. Our core hypothesis is that genuine reasoning is demonstrated only when a model follows logical rules despite conflicting prior knowledge. Evaluating seven state-of-the-art LLMs (8B to ultra-large) reveals strong prior knowledge dependence, with performance degrading sharply on counterfactual tasks as reasoning depth grows. This work delineates LLM reasoning boundaries and presents a new paradigm for fine-grained capability assessment.