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 …

Direct alignment from preferences (DAP) methods, such as DPO, have recently emerged as
efficient alternatives to reinforcement learning from human feedback (RLHF), that do not …

Multilingual Large Language Models are capable of using powerful Large Language
Models to handle and respond to queries in multiple languages, which achieves remarkable …

Human preference alignment is a crucial training step to improve the interaction quality of
large language models (LLMs). Existing aligning methods depend on manually annotated …

Aligning large language models (LLMs) with human values is a vital task for LLM
practitioners. Current alignment techniques have several limitations:(1) requiring a large …

Aligning large language models (LLMs) with human values is imperative to mitigate
potential adverse effects resulting from their misuse. Drawing from the sociological insight …

When prompting a language model (LM), users frequently expect the model to adhere to a
set of behavioral principles across diverse tasks, such as producing insightful content while …

Reinforcement learning from human feedback (RLHF) has emerged as a powerful technique
to make large language models (LLMs) more capable in complex settings. RLHF proceeds …

Conversational search, unlike single-turn retrieval tasks, requires understanding the current
question within a dialogue context. The common approach of rewrite-then-retrieve aims to …

Aligning large language models (LLMs) with human expectations without human-annotated
preference data is an important problem. In this paper, we propose a method to evaluate the …