Making language models bigger does not inherently make them better at following a user's
intent. For example, large language models can generate outputs that are untruthful, toxic, or …

Learning from human preferences is important for language models to match human needs
and to align with human and social values. Prior works have achieved remarkable …

Large language models can perform new tasks in a zero-shot fashion, given natural
language prompts that specify the desired behavior. Such prompts are typically hand …

Large language models (LLMs) have shown impressive success in various applications.
However, these models are often not well aligned with human intents, which calls for …

Reliably controlling the behavior of large language models (LLMs) is a pressing open
problem. Existing methods include supervised finetuning, reinforcement learning from …

Language models (LMs) can be directed to perform target tasks by using labeled examples
or natural language prompts. But selecting examples or writing prompts for can be …

Large language model (LLM) prompting is a promising new approach for users to create
and customize their own chatbots. However, current methods for steering a chatbot's …

When prompting language models to complete a task, users often leave important aspects
unsaid. While asking questions could resolve this ambiguity\citep [GATE;][]{li2023eliciting} …

Abstract Language models (LMs) are pretrained to imitate text from large and diverse
datasets that contain content that would violate human preferences if generated by an LM …

By conditioning on natural language instructions, large language models (LLMs) have
displayed impressive capabilities as general-purpose computers. However, task …