In the rapidly evolving domain of Natural Language Generation (NLG) evaluation,
introducing Large Language Models (LLMs) has opened new avenues for assessing …

In the rapidly evolving field of large language models (LLMs), data augmentation (DA) has
emerged as a pivotal technique for enhancing model performance by diversifying training …

In the rapidly evolving domain of Natural Language Generation (NLG) evaluation,
introducing Large Language Models (LLMs) has opened new avenues for assessing …

With the rapid advancement of large language models (LLMs), information retrieval (IR)
systems, such as search engines and recommender systems, have undergone a significant …

This paper proposes DeepSynth, a method for effective training of deep reinforcement
learning agents when the reward is sparse or non-Markovian, but at the same time progress …

The rapid progress in Large Language Models (LLMs) poses potential risks such as
generating unethical content. Assessing LLMs' values can help expose their misalignment …

Large language models (LLMs) demonstrate impressive performance on a wide variety of
tasks, but they often struggle with tasks that require multi-step reasoning or goal-directed …

This paper investigates the impact of using first names in Large Language Models (LLMs)
and Vision Language Models (VLMs), particularly when prompted with ethical decision …

While large language models (LLMs) have been used for automated grading, they have not
yet achieved the same level of performance as humans, especially when it comes to grading …

Large language models (LLMs) demonstrate impressive performance on a wide variety of
tasks, but they often struggle with tasks that require multi-step reasoning or goal-directed …