This paper embarks on an exploration into the Large Language Model (LLM) datasets,
which play a crucial role in the remarkable advancements of LLMs. The datasets serve as …

The rapid development of Large Language Models (LLMs) demonstrates remarkable
multilingual capabilities in natural language processing, attracting global attention in both …

Abstract Direct Preference Optimization (DPO) is a widely used offline preference
optimization algorithm that reparameterizes reward functions in reinforcement learning from …

Efficient fine-tuning is vital for adapting large language models (LLMs) to downstream tasks.
However, it requires non-trivial efforts to implement these methods on different models. We …

Large language models (LLMs), exemplified by ChatGPT, have gained considerable
attention for their excellent natural language processing capabilities. Nonetheless, these …

Automated red teaming holds substantial promise for uncovering and mitigating the risks
associated with the malicious use of large language models (LLMs), yet the field lacks a …

Since the advent of personal computing devices, intelligent personal assistants (IPAs) have
been one of the key technologies that researchers and engineers have focused on, aiming …

Since the release of T\" ULU [Wang et al., 2023b], open resources for instruction tuning have
developed quickly, from better base models to new finetuning techniques. We test and …

In the age of large-scale language models, benchmarks like the Massive Multitask
Language Understanding (MMLU) have been pivotal in pushing the boundaries of what AI …

Large language models (LLMs) have gained considerable attention for their excellent
natural language processing capabilities. Nonetheless, these LLMs present many …