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 …

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

Data plays a fundamental role in the training of Large Language Models (LLMs). Effective
data management, particularly in the formulation of a well-suited training dataset, holds …

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 …

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

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

Supervised fine-tuning (SFT) is crucial for aligning Large Language Models (LLMs) with
human instructions. The primary goal during SFT is to select a small yet representative …

Instruction tuning helps improve pretrained large language models (LLMs) in terms of the
responsiveness to human instructions, which is benefited from diversified instruction data …

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 …

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 …