Neural Machine Translation (NMT) has seen tremendous growth in the last ten years since
the early 2000s and has already entered a mature phase. While considered the most widely …

The field of machine translation (MT), the automatic translation of written text from one
natural language into another, has experienced a major paradigm shift in recent years …

Reinforcement learning from human feedback (RLHF) is an effective technique for aligning
large language models (LLMs) to human preferences, but gathering high-quality human …

Language models (LMs) have recently shown remarkable performance on reasoning tasks
by explicitly generating intermediate inferences, eg, chain-of-thought prompting. However …

AI alignment in the shape of Reinforcement Learning from Human Feedback (RLHF) is
increasingly treated as a crucial ingredient for high performance large language models …

Deep reinforcement learning (DRL) integrates the feature representation ability of deep
learning with the decision-making ability of reinforcement learning so that it can achieve …

Despite the seeming success of contemporary grounded text generation systems, they often
tend to generate factually inconsistent text with respect to their input. This phenomenon is …

In recent years some researchers have explored the use of reinforcement learning (RL)
algorithms as key components in the solution of various natural language processing (NLP) …

Reinforcement learning from human feedback (RLHF) has proven effective in aligning large
language models (LLMs) with human preferences, but gathering high-quality preference …

Non-autoregressive (NAR) generation, which is first proposed in neural machine translation
(NMT) to speed up inference, has attracted much attention in both machine learning and …