Large Language Models (LLMs) are powerful in language tasks but struggle with high-quality human data. A study proposes a self-training technique, ReST𝐃𝑀, using model-generated synthetic data, which enhances language models’ performance. ReST𝐃𝑀 improves math and code generation skills significantly, surpassing the effectiveness of human-provided data but risks overfitting after multiple cycles. The study is credited to Google DeepMind researchers.
Transforming Language Learning with Large Language Models (LLMs)
Generating High-Quality Human-Level Text with LLMs
Large Language Models (LLMs) have shown remarkable capabilities in producing human-like text and performing various language tasks. However, obtaining high-quality human data remains a challenge for further improving their performance.
Overcoming the Data Barrier with Model-Generated Synthetic Data
To address the obstacle of acquiring human-collected data, model-generated synthetic data offers a scalable and cost-effective solution if its quality can be ensured.
Introducing Reinforced Self-Training (ReST𝐃𝑀)
Researchers from Google Deepmind and Mila propose a practical self-training technique for language models, ReST𝐃𝑀, which leverages model-generated data to enhance performance in areas like machine translation, semantic parsing, and preference alignment.
Enhancing Language Models with ReST𝐃𝑀
ReST𝐃𝑀 involves a straightforward process of creating samples from the model and assessing them using a scoring mechanism. This innovative approach has shown efficacy in improving language models for tasks like code generation and mathematical problem-solving.
Benefits of ReST𝐃𝑀
Models refined with ReST𝐃𝑀 demonstrate superior performance compared to those trained on human-supplied data, particularly in mathematical reasoning and code generation. Additionally, these refined models show enhanced capabilities in pass@k, majority voting, and performance on various benchmarks.
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