Selective Reflection-Tuning: Student-Selected Data Recycling for LLM Instruction-Tuning¶
Arxiv Link - 2024-06-07 20:23:21
Abstract¶
Instruction tuning is critical to large language models (LLMs) for achieving better instruction following and task adaptation capabilities but its success heavily relies on the training data quality. Many recent methods focus on improving the data quality but often overlook the compatibility of the data with the student model being finetuned. This paper introduces Selective Reflection-Tuning, a novel paradigm that synergizes a teacher LLM's reflection and introspection for improving existing data quality with the data selection capability of the student LLM, to automatically refine existing instruction-tuning data. This teacher-student collaboration produces high-quality and student-compatible instruction-response pairs, resulting in sample-efficient instruction tuning and LLMs of superior performance. Selective Reflection-Tuning is a data augmentation and synthesis that generally improves LLM finetuning and self-improvement without collecting brand-new data. We apply our method to Alpaca and WizardLM data and achieve much stronger and top-tier 7B and 13B LLMs.
Socials¶
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| 🚀 Exciting advancements in large language models! 🌟 Improving instruction tuning for LLMs is crucial for enhancing their performance, but it heavily relies on the quality of training data. Check out this groundbreaking paper introducing Selective Reflection-Tuning, a novel paradigm that leverages teacher-student collaboration to automatically refine instruction-tuning data, resulting in high-quality and student-compatible instruction-response pairs. This innovative approach boosts LLMs' performance and efficiency without the need for additional data collection. Read more about this cutting-edge research and its impact on Alpaca and WizardLM data at: http://arxiv.org/abs/2402.10110v2 #AI #NLP #LLMs #DataQuality #Research #Innovation |
🚀 Exciting research alert! Learn about Selective Reflection-Tuning, a novel paradigm enhancing instruction tuning for large language models (LLMs). This method improves data quality and student model compatibility, boosting LLM performance. Check out the paper here: http://arxiv.org/abs/2402.10110v2 #AI #NLP #LLMs #Research #TechInnovation |