Text Generation aims to produce plausible and readable text in human language from input
data. The resurgence of deep learning has greatly advanced this field, in particular, with the …

Machine-generated text is increasingly difficult to distinguish from text authored by humans.
Powerful open-source models are freely available, and user-friendly tools that democratize …

Language is essentially a complex, intricate system of human expressions governed by
grammatical rules. It poses a significant challenge to develop capable AI algorithms for …

Large language models can be prompted to pro-duce fluent output for a wide range of tasks
without being specifically trained to do so. Nevertheless, it is notoriously difficult to control …

The inference process in Large Language Models (LLMs) is often limited due to the absence
of parallelism in the auto-regressive decoding process, resulting in most operations being …

Despite the growing success of diffusion models in continuous-valued domains (eg,
images), similar efforts for discrete domains such as text have yet to match the performance …

The recent performance leap of Large Language Models (LLMs) opens up new
opportunities across numerous industrial applications and domains. However, erroneous …

Large language models (LLMs) have led to a surge in collaborative writing with model
assistance. As different users incorporate suggestions from the same model, there is a risk of …

With the advance of language models, privacy protection is receiving more attention.
Training data extraction is therefore of great importance, as it can serve as a potential tool to …

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 …