Large Language Models (LLMs) excel in various tasks, but they rely on carefully crafted
prompts that often demand substantial human effort. To automate this process, in this paper …

Despite their impressive capabilities, large language models (LLMs) have been observed to
generate responses that include inaccurate or fabricated information, a phenomenon …

Over the past year, the emergence of state-of-the-art large language models (LLMs) in tools
like ChatGPT has ushered in a rapid acceleration in artificial intelligence (AI) innovation …

Grammatical error correction (GEC) is a promising task aimed at correcting errors in a text.
Many methods have been proposed to facilitate this task with remarkable results. However …

Instruction-tuning can be substantially optimized through enhanced diversity, resulting in
models capable of handling a broader spectrum of tasks. However, existing data employed …

Drawing insights from the field of innovation economics, we discuss the likely competitive
environment shaping generative AI advances. Central to our analysis are the concepts of …

While Large Language Models (LLMs) have demonstrated exceptional multitasking abilities,
fine-tuning these models on downstream, domain-specific datasets is often necessary to …

Query performance prediction (QPP) aims to estimate the retrieval quality of a search system
for a query without human relevance judgments. Previous QPP methods typically return a …

We introduce NaSGEC, a new dataset to facilitate research on Chinese grammatical error
correction (CGEC) for native speaker texts from multiple domains. Previous CGEC research …

Grammatical Error Correction (GEC) systems play a vital role in assisting people with their
daily writing tasks. However, users may sometimes come across a GEC system that initially …