Large Language Models (LLMs) are becoming increasingly integrated into our lives. Hence,
it is important to understand the biases present in their outputs in order to avoid perpetuating …

Large language models produce human-like text that drive a growing number of
applications. However, recent literature and, increasingly, real world observations, have …

Deep neural networks often learn unintended bias during training, which might have harmful
effects when deployed in realworld settings. This work surveys 214 papers related to …

The introduction of pre-trained large language models (LLMs) has transformed NLP by fine-
tuning task-specific datasets, enabling notable advancements in news classification …

Large language models are becoming increasingly integrated into our lives. Hence, it is
important to understand the biases present in their outputs in order to avoid perpetuating …

Sentiment analysis is a vital component of natural language processing (NLP), enabling the
classification of text into positive, negative, or neutral sentiments. It is widely used in …

Enjoyment is a crucial yet complex indicator of positive user experience in Human-Robot
Interaction (HRI). While manual enjoyment annotation is feasible, developing reliable …

Large language models (LLMs) often generate biased outputs containing offensive, toxic, or
stereotypical text. Existing LLM alignment methods such as reinforcement learning from …

Machine psychology aims to reconstruct the mindset of Large Language Models (LLMs), ie
how these artificial intelligences perceive and associate ideas. This work introduces …

Image forensics research has recently witnessed a lot of advancements toward developing
computational models capable of accurately detecting natural images captured by cameras …