Large Language Models (LLMs) have shown remarkable performances on a wide range of
natural language understanding and generation tasks. We observe that the LLMs provide …

Temporal reasoning is a crucial natural language processing (NLP) task, providing a
nuanced understanding of time-sensitive contexts within textual data. Although recent …

Our physical world is constantly evolving over time, rendering challenges for pre-trained
language models to understand and reason over the temporal contexts of texts. Existing …

In this paper, we tackle the significant challenge of temporal knowledge reasoning in Large
Language Models (LLMs), an area where such models frequently encounter difficulties …

Knowledge Base Question Answering (KBQA) has been a long-standing field to answer
questions based on knowledge bases. Recently, the evolving dynamics of knowledge have …

Large Language Models (LLMs) are increasingly ubiquitous, yet their ability to retain and
reason about temporal information remains limited, hindering their application in real-world …

Abstract Large Language Models (LLMs) are increasingly ubiquitous, yet their ability to
retain and reason about temporal information remains limited, hindering their application in …

Temporal reasoning is fundamental for large language models (LLMs) to comprehend the
world. Current temporal reasoning datasets are limited to questions about single or isolated …

Large language models (LLMs) for African languages perform worse compared to their
performance in high-resource languages. To address this issue, we introduce AfriInstruct …

Large language models (LLMs) have become the norm in natural language processing
(NLP), excelling in few-shot in-context learning (ICL) with their remarkable abilities …