The review aims to examine the current state of recognizing textual entailment (RTE)
research and summarize the state-of-the-art methods in the development of natural …

Natural Language Inference (NLI) has been extensively studied by the NLP community as a
framework for estimating the semantic relation between sentence pairs. While early work …

Many LLMs are trained to perform zero-shot or few-shot inference using instruction-based
prompts. Crafting prompts for these LLMs typically requires the user to provide a detailed …

Investigating whether pre-trained language models (LMs) can function as knowledge bases
(KBs) has raised wide research interests recently. However, existing works focus on simple …

The rapid development of Large Language Models (LLMs) and the emergence of novel
abilities with scale have necessitated the construction of holistic, diverse and challenging …

Large-scale pre-trained language models have garnered significant attention in recent years
due to their effectiveness in extracting sentence representations. However, most pre-trained …

We introduce a collection of recognizing textual entailment (RTE) datasets focused on
figurative language. We leverage five existing datasets annotated for a variety of figurative …

Recently, research on inference methods has been actively conducted to use language
models more effectively for studying natural language understanding. Inference in language …

Abstract Recently Large Language Models (LLMs) such as GPT-3, ChatGPT, and FLAN
have led to impressive progress in Natural Language Inference (NLI) tasks. However, these …

Motivation of this work is a data-centric approach of improving model accuracy by improving
data quality instead of improving model architecture. The idea is to improve dataset with …