Assessment and evaluation have long been critical challenges in artificial intelligence (AI)
and natural language processing (NLP). However, traditional methods, whether matching …

Social identity biases, particularly the tendency to favor one's own group (ingroup solidarity)
and derogate other groups (outgroup hostility), are deeply rooted in human psychology and …

This paper surveys research in the rapidly growing field of enhancing large language
models (LLMs) with reinforcement learning (RL), a technique that enables LLMs to improve …

LLMs are typically trained to answer user questions or follow instructions similarly to how
human experts respond. However, in the standard alignment framework they lack the basic …

Preference tuning is a crucial process for aligning deep generative models with human
preferences. This survey offers a thorough overview of recent advancements in preference …

As language models become integral to critical workflows, assessing their behavior remains
a fundamental challenge--human evaluation is costly and noisy, while automated metrics …

Most alignment research today focuses on designing new learning algorithms using
datasets like Anthropic-HH, assuming human feedback data is inherently reliable. However …

We present Fanar, a platform for Arabic-centric multimodal generative AI systems, that
supports language, speech and image generation tasks. At the heart of Fanar are Fanar Star …

In the development of Large Language Models (LLMs), considerable attention has been
given to the quality of training datasets. However, the role of tokenizers in the LLM training …

Reinforcement learning with human feedback (RLHF) is shown to largely benefit from
precise reward models (RMs). However, recent studies in reward modeling schemes are …