In recent years, generative transformers have become increasingly prevalent in the field of
artificial intelligence, especially within the scope of natural language processing. This paper …

Ever since the Turing Test was proposed in the 1950s, humans have explored the mastering
of language intelligence by machine. Language is essentially a complex, intricate system of …

Large Language Models (LLMs) employ auto-regressive decoding that requires sequential
computation, with each step reliant on the previous one's output. This creates a bottleneck …

This study investigates the application of large language models (LLMs), specifically GPT-
3.5 and GPT-4, with Chain-of-Though (CoT) in the automatic scoring of student-written …

Recent work suggests that large language models may implicitly learn world models. How
should we assess this possibility? We formalize this question for the case where the …

While recent studies have looked into the abilities of large language models in various
benchmark tasks, including question generation, reading comprehension, multilingual and …

Decoding methods play an indispensable role in converting language models from next-
token predictors into practical task solvers. Prior research on decoding methods, primarily …

One of the most striking findings in modern research on large language models (LLMs) is
that scaling up compute during training leads to better results. However, less attention has …

Automatic summarization is a key area in natural language processing (NLP) and machine
learning which attempts to generate informative summaries of articles and documents …

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