
Kun is a novel self-training method that leverages instruction back-translation and answer polishment to automatically generate large-scale, high-quality Chinese instruction-tuning datasets for LLMs, reducing the reliance on manual annotation.


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kun-a-self-training-approach-for-generating-high-quality-chinese-instruction-tuning-datasets-for-large-language-models-using-instruction-back-translation-and-answer-polishment


Kun: A Self-Training Approach for Generating High-Quality Chinese Instruction-Tuning Datasets for Large Language Models Using Instruction Back-Translation and Answer Polishment


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This paper introduces IDEA-MCTS, a novel framework that leverages Monte Carlo Tree Search (MCTS) and evaluation models to automatically generate high-quality, diverse, and complex instruction data for improving the instruction-following abilities of large language models (LLMs).


idea-mcts-a-framework-for-optimizing-instruction-synthesis-using-monte-carlo-tree-search-and-evaluation-models


IDEA-MCTS: A Framework for Optimizing Instruction Synthesis Using Monte Carlo Tree Search and Evaluation Models
