This survey addresses the crucial issue of factuality in Large Language Models (LLMs). As
LLMs find applications across diverse domains, the reliability and accuracy of their outputs …

Large language models (LLMs) have been recently leveraged as training data generators
for various natural language processing (NLP) tasks. While previous research has explored …

Large language models (LLMs), despite their remarkable progress across various general
domains, encounter significant barriers in medicine and healthcare. This field faces unique …

We explore how continued pre-training on domain-specific corpora influences large
language models, revealing that training on the raw corpora endows the model with domain …

The trustworthiness of machine learning has emerged as a critical topic in the field,
encompassing various applications and research areas such as robustness, security …

Artificial intelligence (AI) has significantly impacted various fields. Large language models
(LLMs) like GPT-4, BARD, PaLM, Megatron-Turing NLG, Jurassic-1 Jumbo etc., have …

The increasing use of AI-generated content has highlighted the critical issue of
hallucinations, where models produce factually incorrect or misleading outputs. Addressing …

Abstract The proliferation of Large Language Models (LLMs) has driven considerable
interest in fine-tuning them with domain-specific data to create specialized language …

Large language models (LLMs) have shown great success in various Natural Language
Processing (NLP) tasks, whist they still need updates after deployment to fix errors or keep …

Adapting pretrained language models to novel domains, such as clinical applications,
traditionally involves retraining their entire set of parameters. Parameter-Efficient Fine …