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What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning
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2312.15685
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16
DolphCoder: Echo-Locating Code Large Language Models with Diverse and Multi-Objective Instruction Tuning
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2402.09136
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1
CS-Bench: A Comprehensive Benchmark for Large Language Models towards Computer Science Mastery
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2406.08587
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Aqulia-Med LLM: Pioneering Full-Process Open-Source Medical Language Models
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2406.12182
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Automatic Instruction Evolving for Large Language Models
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2406.00770
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Excited to announce WizardLM new Paper: Auto Evol-Instruct!
🐦 Twitter: https://x.com/WizardLM_AI/status/1812857977122202087
📃 Paper: https://arxiv.org/pdf/2406.00770
🤖 1. Fully AI-Powered Pipeline
Auto Evol-Instruct automatically involves an iterative process of optimizing an Evol-Instruct V1 into an optimal one. The pipeline consists of two critical stages: Evol Trajectory Analysis, where the optimizer LLM analyzes the issues and failures exposed in instruction evolution performed by the evol LLM, and Evolving Method Optimization, where the optimizer LLM addresses these issues to progressively develop an effective evolving method. The optimal evolving method is then used to convert the entire instruction dataset into more diverse and complex forms, facilitating improved instruction tuning.
📈2. Scaling Evol-Instruct with Arena Learning
With Auto Evol-Instruct, the evolutionary synthesis data of WizardLM-2 has scaled up from WizardLM-1 to dozens of domains, covering tasks in all aspects of large language models. This allows Arena Learning to train and learn from an almost infinite pool of high-difficulty instruction data, fully unlocking all the potential of Arena Learning.
Excited to announce WizardLM new Paper: Auto Evol-Instruct!
🐦 Twitter: https://x.com/WizardLM_AI/status/1812857977122202087
📃 Paper: https://arxiv.org/pdf/2406.00770
🤖 1. Fully AI-Powered Pipeline
Auto Evol-Instruct automatically involves an iterative process of optimizing an Evol-Instruct V1 into an optimal one. The pipeline consists of two critical stages: Evol Trajectory Analysis, where the optimizer LLM analyzes the issues and failures exposed in instruction evolution performed by the evol LLM, and Evolving Method Optimization, where the optimizer LLM addresses these issues to progressively develop an effective evolving method. The optimal evolving method is then used to convert the entire instruction dataset into more diverse and complex forms, facilitating improved instruction tuning.
📈2. Scaling Evol-Instruct with Arena Learning
With Auto Evol-Instruct, the evolutionary synthesis data of WizardLM-2 has scaled up from WizardLM-1 to dozens of domains, covering tasks in all aspects of large language models. This allows Arena Learning to train and learn from an almost infinite pool of high-difficulty instruction data, fully unlocking all the potential of Arena Learning.
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