Statistical machine translation (SMT) treats the translation of natural language as a machine
learning problem. By examining many samples of human-produced translation, SMT …

A brain–computer interface that decodes continuous language from non-invasive recordings
would have many scientific and practical applications. Currently, however, non-invasive …

ACM: Digital Library: Communications of the ACM ACM Digital Library Communications of the
ACM Volume 67, Number 4 (2024), Pages 50-59 The Science of Detecting LLM-Generated Text …

Conditional language models are predominantly trained with maximum likelihood estimation
(MLE), giving probability mass to sparsely observed target sequences. While MLE trained …

Autoregressive large language models (LLMs) compress knowledge from their training data
through next-token conditional distributions. This limits tractable querying of this knowledge …

The complete biosynthetic pathways are unknown for most natural products (NPs), it is thus
valuable to make computer-aided bio-retrosynthesis predictions. Here, a navigable and user …

Pre-trained code language models have achieved promising performance in code
generation and improved the programming efficiency of human developers. However, their …

Although recent advancements in large language models (LLMs) have significantly
improved their performance on various tasks, they still face challenges with complex and …

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 …

The Traveling Salesman Problem (TSP) is the most popular and most studied combinatorial
problem, starting with von Neumann in 1951. It has driven the discovery of several …