Large language models (LLMs) such as GPT-3 and GPT-4 are powerful but their weights are
often publicly unavailable and their immense sizes make the models difficult to be tuned with …

Large language models (LLMs) have demonstrated remarkable potential in handling
multilingual machine translation (MMT). In this paper, we systematically investigate the …

Autonomous interaction with the computer has been a longstanding challenge with great
potential, and the recent proliferation of large language models (LLMs) has markedly …

With emerging topics (eg, COVID-19) on social media as a source for the spreading
misinformation, overcoming the distributional shifts between the original training domain (ie …

Self-Consistency samples diverse reasoning chains with answers and chooses the final
answer by majority voting. It is based on forward reasoning and cannot further improve …

Multimodal Large Language Models (MLLMs) have shown impressive abilities in interacting
with visual content with myriad potential downstream tasks. However, even though a list of …

The recent development and success of Large Language Models (LLMs) necessitate an
evaluation of their performance across diverse NLP tasks in different languages. Although …

Multilingual transfer ability, which reflects how well the models fine-tuned on one source
language can be applied to other languages, has been well studied in multilingual pre …

Large language models (LLMs) can use in-context demonstrations to improve performance
on zero-shot tasks. However, selecting the best in-context examples is challenging because …

Neural language models are probabilistic models of human text. They are predominantly
trained using maximum likelihood estimation (MLE), which is equivalent to minimizing the …