Causal inference has shown potential in enhancing the predictive accuracy, fairness,
robustness, and explainability of Natural Language Processing (NLP) models by capturing …

Large language models (LLMs) are vulnerable to adversarial attacks that add malicious
tokens to an input prompt to bypass the safety guardrails of an LLM and cause it to produce …

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

The prompt-based learning paradigm, which bridges the gap between pre-training and fine-
tuning, achieves state-of-the-art performance on several NLP tasks, particularly in few-shot …

The language models, especially the basic text classification models, have been shown to
be susceptible to textual adversarial attacks such as synonym substitution and word …

Large language models (LLMs) have exhibited remarkable efficacy and proficiency in a
wide array of NLP endeavors. Nevertheless, concerns are growing rapidly regarding the …

In-context learning, a paradigm bridging the gap between pre-training and fine-tuning, has
demonstrated high efficacy in several NLP tasks, especially in few-shot settings. Despite …

Recently, various parameter-efficient fine-tuning (PEFT) strategies for application to
language models have been proposed and successfully implemented. However, this raises …

Although large language models (LLMs) have achieved great success in vast real-world
applications, their vulnerabilities towards noisy inputs have significantly limited their uses …

Despite the recent success of large pretrained language models in NLP, they are
susceptible to adversarial examples. Concurrently, several studies on adversarial images …