Transformer-based language models (LMs) have demonstrated remarkable performance on many natural language tasks, yet to what extent LMs possess the capability of generalizing to unseen logical rules remains not explored sufficiently. In classical logic category, abductive, deductive and inductive (ADI) reasoning are defined as the fundamental reasoning types, sharing the identical reasoning primitives and properties, and some research have proposed that there exists mutual generalization across them. However, in the field of natural language processing, previous research generally study LMs’ ADI reasoning capabilities separately, overlooking the generalization across them. To bridge this gap, we propose UniADILR, a novel logical reasoning dataset crafted for assessing the generalization capabilities of LMs across different logical rules. Based on UniADILR, we conduct extensive investigations from various perspectives of LMs’ performance on ADI reasoning. The experimental results reveal the weakness of current LMs in terms of extrapolating to unseen rules and inspire a new insight for future research in logical reasoning.

Recent advances in handling long sequences have unlocked new possibilities for long-context in-context learning (ICL). While existing research predominantly focuses on performance gains driven by additional in-context examples, the impact on the trustworthiness of generated responses remains underexplored. This paper addresses this gap by investigating how increased examples influence predictive uncertainty—an essential aspect in trustworthiness. We begin by systematically quantifying uncertainty across different “shot” configurations in ICL, emphasizing the role of example quantity. Through uncertainty decomposition, we introduce a novel perspective on performance enhancement, focusing on epistemic uncertainty (EU). Our results reveal that additional examples reduce total uncertainty in both simple and complex tasks by injecting task-specific knowledge, thereby diminishing EU and enhancing performance. For complex tasks, these advantages emerge only after addressing the increased noise and uncertainty associated with longer inputs. Finally, we investigate the progression of internal confidence across layers, uncovering the underlying mechanisms that drive the reduction in uncertainty.

In this paper, we investigate whether Large Language Models (LLMs) actively recall or retrieve their internal repositories of factual knowledge when faced with reasoning tasks. Through an analysis of LLMs’ internal factual recall at each reasoning step via Knowledge Neurons, we reveal that LLMs fail to harness the critical factual associations under certain circumstances. Instead, they tend to opt for alternative, shortcut-like pathways to answer reasoning questions. By manually manipulating the recall process of parametric knowledge in LLMs, we demonstrate that enhancing this recall process directly improves reasoning performance whereas suppressing it leads to notable degradation. Furthermore, we assess the effect of Chain-of-Thought (CoT) prompting, a powerful technique for addressing complex reasoning tasks. Our findings indicate that CoT can intensify the recall of factual knowledge by encouraging LLMs to engage in orderly and reliable reasoning. Furthermore, we explored how contextual conflicts affect the retrieval of facts during the reasoning process to gain a comprehensive understanding of the factual recall behaviors of LLMs. Code and data will be available soon.