Preference optimization, particularly through Reinforcement Learning from Human
Feedback (RLHF), has achieved significant success in aligning Large Language Models …

Instruction tuning plays a critical role in aligning large language models (LLMs) with human
preference. Despite the vast amount of open instruction datasets, naively training a LLM on …

Large Language Models (LLMs) have demonstrated great potential in complex reasoning
tasks, yet they fall short when tackling more sophisticated challenges, especially when …

Despite the notable advancements of existing prompting methods, such as In-Context
Learning and Chain-of-Thought for Large Language Models (LLMs), they still face …

Instruction tuning (IT) is crucial to tailoring large language models (LLMs) towards human-
centric interactions. Recent advancements have shown that the careful selection of a small …

This paper introduces the innovative" LLMs-as-Instructors" framework, which leverages the
advanced Large Language Models (LLMs) to autonomously enhance the training of smaller …

Navigating the intricacies of data management in the era of Large Language Models (LLMs)
presents both challenges and opportunities for database and data management …

Instruction data is crucial for improving the capability of Large Language Models (LLMs) to
align with human-level performance. Recent research LIMA demonstrates that alignment is …

The pre-trained Large Language Models (LLMs) can be adapted for many downstream
tasks and tailored to align with human preferences through fine-tuning. Recent studies have …

Experimental design techniques such as active search and Bayesian optimization are
widely used in the natural sciences for data collection and discovery. However, existing …