The emergence of large language models (LLMs) has marked a significant breakthrough in
natural language processing (NLP), fueling a paradigm shift in information acquisition …

Reinforcement learning (RL) is typically viewed as the problem of estimating single-step
policies (for model-free RL) or single-step models (for model-based RL), leveraging the …

Large-scale language models are rapidly improving, performing well on a wide variety of
tasks with little to no customization. In this work we investigate how language models can …

With direct access to human-written reference as memory, retrieval-augmented generation
has achieved much progress in a wide range of text generation tasks. Since better memory …

Today's probabilistic language generators fall short when it comes to producing coherent
and fluent text despite the fact that the underlying models perform well under standard …

Neural encoder-decoder models for language generation can be trained to predict words
directly from linguistic or non-linguistic inputs. When generating with these so-called end-to …

Despite the progress in machine translation quality estimation and evaluation in the last
years, decoding in neural machine translation (NMT) is mostly oblivious to this and centers …

The uniform information density (UID) hypothesis posits a preference among language
users for utterances structured such that information is distributed uniformly across a signal …

Recent advances of powerful Language Models have allowed Natural Language
Generation (NLG) to emerge as an important technology that can not only perform traditional …

Training learnable metrics using modern language models has recently emerged as a
promising method for the automatic evaluation of machine translation. However, existing …