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аналитика - Instruction Tuning for Large Language Models
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 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.
IDEA-MCTS: A Framework for Optimizing Instruction Synthesis Using Monte Carlo Tree Search and Evaluation Models
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).
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