Summary of Rethinking Data Synthesis: a Teacher Model Training Recipe with Interpretation, by Yifang Chen et al.
Rethinking Data Synthesis: A Teacher Model Training Recipe with Interpretation
by Yifang Chen, David Zhu, Simon Du, Kevin Jamieson, Yang Liu
First submitted to arxiv on: 27 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 A recent surge in large language model (LLM) training has underscored the necessity for diverse, high-quality instruction data. While many studies focus on generating synthetic data using LLMs, they primarily rely on prompt engineering with standard supervised instruction-finetuned models, which is limited by their optimization for general question-answering/problem-solving rather than data generation. This paper proposes a paradigm shift, NOMAD, to train models specifically for data generation, demonstrating that this task differs significantly from training classical LMs. The authors identify two key factors: no-prompt-masked training and proper training set size selection. Their method, NOMAD, achieves substantial improvements over baselines in TriviaQA (+4%) and GSM8K (+2%) with limited training data. The study also offers new insights by interpreting synthetic data through the lenses of “relevance” and “novelty”. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are super smart computers that can learn from big amounts of text. Recently, scientists have been trying to make these models better at generating new text that looks like real text. But so far, they’ve only used a simple trick called prompt engineering. This trick helps the model understand what kind of text it should generate, but it’s not very good at making new text that’s actually useful. In this study, scientists came up with a new way to train language models specifically for generating text. They call it NOMAD. The new method works better than old methods in some cases, and they even figured out why it works better. |
Keywords
» Artificial intelligence » Large language model » Optimization » Prompt » Question answering » Supervised » Synthetic data
