Large language models (LLMs) have a wealth of knowledge that allows them to excel in
various Natural Language Processing (NLP) tasks. Current research focuses on enhancing …

Direct preference optimization (DPO) is a successful fine-tuning strategy for aligning large
language models with human preferences without the need to train a reward model or …

While Neural Machine Translation (NMT) represents the leading approach to Machine
Translation (MT), the outputs of NMT models still require translation post-editing to rectify …

Large Language Models (LLMs) usually suffer from knowledge cutoff or fallacy issues, which
means they are unaware of unseen events or generate text with incorrect facts owing to the …

Large language models (LLMs) have made significant strides in various natural language
processing (NLP) tasks. Recent research shows that the moderately-sized LLMs often …

Open-domain question answering (QA) systems are often built with retrieval modules.
However, retrieving passages from a given source is known to suffer from insufficient …

Recent Language Models (LMs) have shown impressive capabilities in generating texts with
the knowledge internalized in parameters. Yet, LMs often generate the factually incorrect …

In the face of climate change, are companies really taking substantial steps toward more
sustainable operations? A comprehensive answer lies in the dense, information-rich …

The success of ChatGPT has ignited an AI race, with researchers striving to develop new
large language models (LLMs) that can match or surpass the language understanding and …

Adversarial testing of large language models (LLMs) is crucial for their safe and responsible
deployment. We introduce a novel approach for automated generation of adversarial …