Large language models (LLMs), exemplified by ChatGPT, have gained considerable
attention for their excellent natural language processing capabilities. Nonetheless, these …

Instruction tuning large language models (LLMs) remains a challenging task, owing to the
complexity of hyperparameter selection and the difficulty involved in evaluating the tuned …

Large language models (LLMs) have gained considerable attention for their excellent
natural language processing capabilities. Nonetheless, these LLMs present many …

NLP research on logical reasoning regains momentum with the recent releases of a handful
of datasets, notably LogiQA and Reclor. Logical reasoning is exploited in many probing …

Pre-trained language models (PLMs) are known to improve the generalization performance
of natural language understanding models by leveraging large amounts of data during the …

Counterfactual examples have proven to be valuable in the field of natural language
processing (NLP) for both evaluating and improving the robustness of language models to …

SenticNet is the knowledge base which the sentic computing framework leverages on for
concept-level sentiment analysis. This chapter illustrates how such a resource is built. In …

Based on the foundation of Large Language Models (LLMs), Multilingual LLMs (MLLMs)
have been developed to address the challenges faced in multilingual natural language …

While vision-language models (VLMs) have achieved remarkable performance
improvements recently there is growing evidence that these models also posses harmful …

Motivations for methods in explainable artificial intelligence (XAI) often include detecting,
quantifying and mitigating bias, and contributing to making machine learning models fairer …