Deep learning (DL) techniques have obtained remarkable achievements on various tasks,
such as image recognition, object detection, and language modeling. However, building a …

Natural language processing tasks, such as question answering, machine translation,
reading comprehension, and summarization, are typically approached with supervised …

The current expansion of theory and research on artificial intelligence in management and
organization studies has revitalized the theory and research on decision-making in …

Interacting with humans through multi-turn conversations is a fundamental feature of large
language models (LLMs). However, existing LLM serving engines executing multi-turn …

Statistical language modeling techniques have successfully been applied to large source
code corpora, yielding a variety of new software development tools, such as tools for code …

Subword segmentation is widely used to address the open vocabulary problem in machine
translation. The dominant approach to subword segmentation is Byte Pair Encoding (BPE) …

State-of-the-art models in natural language processing rely on separate rigid subword
tokenization algorithms, which limit their generalization ability and adaptation to new …

Modeling human language requires the ability to not only generate fluent text but also
encode factual knowledge. However, traditional language models are only capable of …

We study an interesting problem in training neural network-based models for natural
language generation tasks, which we call the\emph {representation degeneration problem} …

Word co‐occurrence patterns in language corpora contain a surprising amount of
conceptual knowledge. Large language models (LLMs), trained to predict words in context …