Data annotation is the labeling or tagging of raw data with relevant information, essential for
improving the efficacy of machine learning models. The process, however, is labor-intensive …

Data annotation and synthesis generally refers to the labeling or generating of raw data with
relevant information, which could be used for improving the efficacy of machine learning …

Abstract Large Language Models (LLMs) have achieved unprecedented breakthroughs in
various natural language processing domains. However, the enigmatic``black-box''nature of …

The advent of generative Large Language Models (LLMs) such as ChatGPT has catalyzed
transformative advancements across multiple domains. However, alongside these …

As Large Language Models (LLMs) are increasingly deployed to handle various natural
language processing (NLP) tasks, concerns regarding the potential negative societal …

The rapid advancement of Large Language Models (LLMs) has significantly influenced
various domains, leveraging their exceptional few-shot and zero-shot learning capabilities …

Learning from noisy labels (LNL) is a challenge that arises in many real-world scenarios
where collected training data can contain incorrect or corrupted labels. Most existing …

Despite recent advancements in detecting disinformation generated by large language
models (LLMs), current efforts overlook the ever-evolving nature of this disinformation. In this …

Parameter-efficient fine-tuning (PEFT) has enabled the efficient optimization of cumbersome
language models in real-world settings. However, as datasets in such environments often …

Enhancing model interpretability can address spurious correlations by revealing how
models draw their predictions. Concept Bottleneck Models (CBMs) can provide a principled …