Article
The Rise of Agentic Data Generation
By
β’
β’
81WizardLM
WizardLM
AI & ML interests
NLP, LLM
Recent Activity
liked
a model
about 1 month ago
deepseek-ai/DeepSeek-V3
reacted
to
their
post
with π
7 months ago
π₯ π₯π₯
Excited to announce WizardLM new Paper: Auto Evol-Instruct!
π¦ Twitter: https://x.com/WizardLM_AI/status/1812857977122202087
π Paper: https://arxiv.org/pdf/2406.00770
π€ 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.
π2. 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.
posted
an
update
7 months ago
π₯ π₯π₯
Excited to announce WizardLM new Paper: Auto Evol-Instruct!
π¦ Twitter: https://x.com/WizardLM_AI/status/1812857977122202087
π Paper: https://arxiv.org/pdf/2406.00770
π€ 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.
π2. 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.
Organizations
WizardLM's activity
upvoted
an
article
7 months ago
upvoted
a
collection
10 months ago
upvoted
3
papers
over 1 year ago
WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct
Paper
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2308.09583
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Published
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7
WizardLM: Empowering Large Language Models to Follow Complex Instructions
Paper
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2304.12244
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Published
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14
WizardCoder: Empowering Code Large Language Models with Evol-Instruct
Paper
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2306.08568
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Published
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28