Language models (LMs) have demonstrated remarkable capabilities across a wide range of
tasks in various domains. Despite their impressive performance, the reliability of their output …

Large language models (LLMs) have demonstrated remarkable capabilities across a wide
range of tasks in various domains. Despite their impressive performance, they can be …

Large language models (LLMs) have recently shown great advances in a variety of tasks,
including natural language understanding and generation. However, their use in high …

While large language models are able to retain vast amounts of world knowledge seen
during pretraining, such knowledge is prone to going out of date and is nontrivial to update …

Abstention Ability (AA) is a critical aspect of Large Language Model (LLM) reliability,
referring to an LLM's capability to withhold responses when uncertain or lacking a definitive …

Though state-of-the-art (SOTA) NLP systems have achieved remarkable performance on a
variety of language understanding tasks, they primarily focus on questions that have a …

Subjective tasks in NLP have been mostly relegated to objective standards, where the gold
label is decided by taking the majority vote. This obfuscates annotator disagreement and the …

Large Language Models (LLMs) have achieved remarkable performance across a wide
variety of natural language tasks; however, their large size makes their inference slow and …

Word sense disambiguation (WSD), which aims to determine an appropriate sense for a
target word given its context, is crucial for natural language understanding. Existing …

The task of reading comprehension (RC), often implemented as context-based question
answering (QA), provides a primary means to assess language models' natural language …