Summary of Synthesizing Post-training Data For Llms Through Multi-agent Simulation, by Shuo Tang et al.
Synthesizing Post-Training Data for LLMs through Multi-Agent Simulation
by Shuo Tang, Xianghe Pang, Zexi Liu, Bohan Tang, Rui Ye, Tian Jin, Xiaowen Dong, Yanfeng Wang, Siheng Chen
First submitted to arxiv on: 18 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 This paper presents a novel approach to generating high-quality instruction data for large language models (LLMs) using a multi-agent simulator called MATRIX. The proposed framework, MATRIX-Gen, can automatically generate diverse text-based scenarios that capture real-world human needs in a realistic and scalable manner. This is achieved by leveraging the outputs of the simulator to create controllable and highly realistic data synthesis. The authors demonstrate the effectiveness of their framework through extensive experiments on various benchmarks, including AlpacaEval 2 and Arena-Hard. They show that Llama-3-8B-Base, post-trained on datasets synthesized by MATRIX-Gen with just 20K instruction-response pairs, outperforms Meta’s Llama-3-8B-Instruct model, which was trained on over 10M pairs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us teach big language models to follow human instructions. It’s hard to get good data for this because people are private and it costs a lot to label things. The authors create a special computer program called MATRIX that can make lots of realistic scenarios, like stories or conversations. They use these scenarios to make a new way to generate instruction data that is very realistic. They test their method and show that it works well. Even with just a little bit of training data, their model performs better than one that was trained on much more data. |
Keywords
» Artificial intelligence » Llama
