Summary of Softsrv: Learn to Generate Targeted Synthetic Data, by Giulia Desalvo et al.
SoftSRV: Learn to Generate Targeted Synthetic Data
by Giulia DeSalvo, Jean-Fracois Kagy, Lazaros Karydas, Afshin Rostamizadeh, Sanjiv Kumar
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 We introduce SoftSRV, a novel framework for generating targeted synthetic fine-tuning data to improve task-specific model performance. Given a sample from a target distribution, our approach uses a data-driven loss minimization technique to guide a frozen large language model (LLM) in generating synthetic sequences that are similar to those from the target distribution. Unlike common prompt engineering methods relying on human-engineered templates, SoftSRV provides a practical improvement by avoiding labor-intensive and domain-specific prompts. We empirically evaluate our method against standard baselines for guiding an LLM to generate synthetic data for fine-tuning smaller language models in three domains: coding, math, and reasoning. Our results show that SoftSRV outperforms typical prompt engineering approaches, generating targeted data that leads to significantly better task-specific performance in fine-tuned models. Additionally, SoftSRV-generated data matches the target distribution more closely according to the MAUVE similarity metric. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We developed a new way to help machines learn from fake data that’s similar to real-world examples. This approach, called SoftSRV, takes a sample of what we want the machine to learn and uses it to guide an existing large language model to generate more fake data that matches the target example. Unlike previous methods that rely on human-made prompts, our method is practical and doesn’t require specialized knowledge for different domains. We tested SoftSRV in three areas: coding, math, and reasoning. The results show that our approach outperforms traditional methods by producing better-performing models with more accurate data. |
Keywords
» Artificial intelligence » Fine tuning » Large language model » Prompt » Synthetic data
