Summary of Agentinstruct: Toward Generative Teaching with Agentic Flows, by Arindam Mitra et al.
AgentInstruct: Toward Generative Teaching with Agentic Flows
by Arindam Mitra, Luciano Del Corro, Guoqing Zheng, Shweti Mahajan, Dany Rouhana, Andres Codas, Yadong Lu, Wei-ge Chen, Olga Vrousgos, Corby Rosset, Fillipe Silva, Hamed Khanpour, Yash Lara, Ahmed Awadallah
First submitted to arxiv on: 3 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces AgentInstruct, a framework for generating synthetic data for post-training language models. This approach creates large amounts of diverse and high-quality data by using raw text documents and code files as seeds. The authors demonstrate the utility of AgentInstruct by creating a dataset of 25 million pairs to teach language models various skills, such as text editing, creative writing, and coding. They compare the resulting model Orca-3 to the base model Mistral-7b-Instruct, observing significant improvements across multiple benchmarks, including AGIEval, MMLU, GSM8K, BBH, and AlpacaEval. The paper highlights the potential of AgentInstruct for accelerating language model development and improving their performance on various tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine creating fake data to help teach computers new skills. This paper introduces a way to do just that using real texts and code files as seeds. They show how this method can create lots of different kinds of data to help train language models for jobs like editing, writing stories, and coding. The results are impressive, with the trained model performing better on many tests than others in its class. This new approach has big potential for making computers smarter and more helpful. |
Keywords
* Artificial intelligence * Language model * Synthetic data
