Recent AI-assistant agents, such as ChatGPT, predominantly rely on supervised fine-tuning
(SFT) with human annotations and reinforcement learning from human feedback (RLHF) to …

Supervised Fine-Tuning (SFT) on response demonstrations combined with Reinforcement
Learning from Human Feedback (RLHF) constitutes a powerful paradigm for aligning LLM …

We present a scalable method to build a high quality instruction following language model
by automatically labelling human-written text with corresponding instructions. Our approach …

Aligning large language models (LLMs) with human preferences has proven to drastically
improve usability and has driven rapid adoption as demonstrated by ChatGPT. Alignment …

Adapting pre-trained language models (PrLMs)(eg, BERT) to new domains has gained
much attention recently. Instead of fine-tuning PrLMs as done in most previous work, we …

Large language models are trained in two stages:(1) unsupervised pretraining from raw text,
to learn general-purpose representations, and (2) large scale instruction tuning and …

The rapid development of Large Language Models (LLMs) has substantially expanded the
range of tasks they can address. In the field of Natural Language Processing (NLP) …

Aligning large language models (LLMs) to human values has become increasingly
important as it enables sophisticated steering of LLMs. However, it requires significant …

Large language models (LLMs) have formulated a blueprint for the advancement of artificial
general intelligence. Its primary objective is to function as a human-centric (helpful, honest …

Alignment tuning has become the de facto standard practice for enabling base large
language models (LLMs) to serve as open-domain AI assistants. The alignment tuning …