Jun 2026· arXiv.org· Vol abs/2606.27237· 0 citations· 38 references
Computer Science
TL;DR
The findings suggest that what the model knows and how it is asked are intertwined in parameter space, undermining the "knowledge base"alogy and carrying implications for the reliability and controllability of factual knowledge in LMs.
Abstract
Language models (LMs) capture large amounts of factual knowledge applicable to a wide range of tasks, motivating the view of their parameters as a knowledge base. An important property of knowledge bases is that different queries for the same fact return consistent results, drawing on a single source of truth. We investigate whether LMs satisfy this property through behavioral and mechanistic analyses. Our results suggest that they encode knowledge in a task-specific manner. Behaviorally, facts acquired on one task frequently fail to co-emerge on others during training. Parameter localization experiments suggest a mechanistic explanation, revealing distinct parameter subsets underlying different tasks for the same fact. Finally, we show that chain-of-thought reasoning draws part of its effectiveness from engaging task-specific parameters beyond those tied to the evaluation task. Our findings suggest that what the model knows and how it is asked are intertwined in parameter space, undermining the"knowledge base"analogy and carrying implications for the reliability and controllability of factual knowledge in LMs.
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.
Fangfei Yan, Jianbo Yao, Michael K. Chen et al.· Proceedings of the 32nd ACM...· 1 citation
A new benchmark, KIFI, is designed, which comprises 1032 carefully selected instances from the TRUE and ScreenEval datasets, with key information annotated, and it is shown that LLMs frequently fail to use the appropriate information to make correct decisions.
Xindi Guo, Zhen Xie, Patrick H. Chen· Annual International ACM SIG...· 0 citations
Generative AI Large Language Models (LLMs) such as GPT-4, Claude, and Gemini are reshaping knowledge work across disciplines. Yet these systems exhibit a puzzling paradox: they can pass rigorous professional examinations while simultaneously failing elementary reasoning tasks. This paper presents a condensed theoretical framework explaining how knowledge is created, stored, and retrieved in LLMs through Stochastic Knowledge Aggregation (SKA) – a process fundamentally different from the Systematic Knowledge Scaffolding (SKS) that characterizes human learning. We introduce 19 foundational concepts, three formal theories, and a set of propositions collectively forming the Jagged Knowledge Frontier (JKF) framework. Empirical cases validate the framework and illuminate implications for Knowledge Management (KM). The paper argues that understanding AI knowledge creation is essential for bridging traditional human KM with the emerging discipline of AI Knowledge Management, and for designing governance structures that account for the inherent incompleteness and inconsistency of LLM knowledge.
T. Nguyen· European Conference on Knowl...· 0 citations
It is indicated that RL-trained models not only demonstrate greater awareness of their learned behaviors and stronger generalizability to novel, structurally similar tasks than SFT models but also often exhibit weak alignment between their reasoning traces and final outputs, an effect most pronounced in GRPO-trained models.
Pratham Singla, Shivank Garg, Ayush Singh et al.· Annual Meeting of the Associ...· 0 citations
This work presents the first systematic study of inverse relation directionality in LLMs, using a benchmark consisting of 5,457 instances spanning 27 distinct inverse relation labels and reveals systematic asymmetries in inverse relation classification across LLMs.
LMEnt is released to support studies of knowledge in LMs, including knowledge representations, plasticity, editing, attribution, hallucinations, and learning dynamics, finding that entity co-occurrence and mention forms—which are difficult to study with existing tools—affect learning trends.
Daniela Gottesman, Alon Gilaie-Dotan, Ido Cohen et al.· Transactions of the Associat...· 0 citations