Latent Space: The AI Engineer Podcast - 2024 in Synthetic Data and Smol Models [LS Live @ NeurIPS]
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The talk at NeurIPS 2024 highlights the explosion of synthetic data usage in AI, particularly in pre-training and post-training phases. Synthetic data is now integral to the large language model (LLM) pipeline, offering control over data generation and reducing reliance on human annotations. This shift allows for entirely synthetic training pipelines, exemplified by Hugging Face's Cosmopedia dataset. Concerns about model collapse due to synthetic data are addressed, with evidence suggesting that synthetic data, when curated carefully, does not degrade model performance. The discussion also covers the rise of small models, which are becoming more efficient and capable of running on consumer devices. These models, such as those developed by Hugging Face and Meta, are trained longer on diverse datasets, achieving performance comparable to larger models. The trend towards smaller, more efficient models is driven by the need for cost-effective and privacy-preserving solutions, with applications in on-device AI becoming more feasible. The talk concludes with a focus on the importance of domain-specific synthetic data and the potential for small models to be fine-tuned for specific tasks, offering a cost-effective alternative to large models.
Key Points:
- Synthetic data is now widely used in AI, reducing the need for human annotations and allowing for controlled data generation.
- Concerns about model collapse due to synthetic data are mitigated by careful curation, with evidence showing no performance degradation.
- Small models are becoming more efficient, capable of running on consumer devices, and offer privacy benefits by keeping data local.
- Training smaller models longer on diverse datasets can achieve performance comparable to larger models, reducing costs.
- Domain-specific synthetic data and fine-tuning small models for specific tasks are emerging trends, offering cost-effective alternatives to large models.
Details:
1. 🎤 Welcome to Latent Space Live
- The event was held at NeurIPS 2024 in Vancouver, marking the first mini-conference for Latent Space Live.
- A survey was conducted with over 900 participants to determine the topics of interest, leading to the selection of top speakers from the Latent Space Network.
- The conference had an in-person attendance of 200 people and over 2,200 online viewers, indicating significant interest and reach.
2. 🔍 Exploring Synthetic Data in LLMs
2.1. Synthetic Data Usage in LLMs
2.2. Rise of Small Models
3. 🧠 Training and Evaluating with Synthetic Pipelines
3.1. Training with Synthetic Data
3.2. Advantages of Synthetic Data
3.3. Concerns about Model Collapse and Synthetic Data
4. 🔄 Reproducing and Diversifying Synthetic Data
- Training models on recent data dumps showed improved performance on NLP benchmarks, indicating that synthetic data did not degrade model quality.
- Using synthetic data for pre-training has gained popularity, with examples like Microsoft's use of large language models to train smaller models, resulting in better performance than larger models.
- Hugging Face attempted to reproduce Microsoft's approach by creating a synthetic dataset, Cosmopedia, with 30 billion tokens, consisting of textbooks, blog posts, and stories.
- Diversity in synthetic datasets is crucial; prompts should include diverse seeds to avoid repetitive outputs, such as generating textbooks related to specific web page extracts.
- Experiments showed that different generation styles benefit specific benchmarks, e.g., college textbooks improved MMLU performance, while middle school textbooks were better for OpenBookQA and Pico.
- Cosmopedia, despite being smaller than FineWeb, consistently outperformed it in training, demonstrating the effectiveness of a well-constructed synthetic dataset.
5. 🔧 Techniques for High-Quality Synthetic Data
- NVIDIA's Nemotron CC generated 1.9 trillion tokens, showcasing the potential of large-scale synthetic datasets.
- The approach involves rephrasing web content using LLMs to create high-quality datasets, such as converting text into Wikipedia-style passages or Q&A formats.
- This method allows the use of smaller models since rewriting doesn't require extensive knowledge, improving efficiency.
- Rewriting samples from C4 datasets into different formats proved more effective than using the original C4 data alone.
- Nemotron CC improved low-quality pages by rewriting them into higher-quality formats like Wikipedia pages, enhancing dataset diversity.
- The Pros approach generates programs to clean and normalize web pages, though it may be less scalable.
- FineWebEDU dataset was created by rating educational content on a scale of 0 to 5, filtering out less educational pages, reducing the dataset from 15 trillion to 1.5 trillion tokens.
- FineWebEDU outperformed other datasets on benchmarks, demonstrating the effectiveness of filtering for high-quality educational content.
- DCLM datasets use classifiers trained on specific datasets like OpenHermes for instruction tuning, achieving high-quality, information-dense datasets.
- Nemotron used an ensemble of classifiers to combine scores and retain only the best pages, resulting in superior datasets.
6. 🤖 Advances in Small Models and Efficiency
6.1. Microsoft's Agent Instruct and Synthetic Data
6.2. Allen AI's Tool Three SFT Mixture and Smalltalk Dataset
6.3. Cohere's Multilingual Data Arbitrage
6.4. Advancements in Small Models
6.5. Efficiency and On-Device Models
7. 📱 On-Device Small Models and Their Benefits
7.1. Meta's Mobile LLM Paper
7.2. Apple Intelligence Tech Report
7.3. NVIDIA's Hybrid Models
7.4. Training and Data Curation
7.5. Benefits of Small Models
7.6. Text Extraction and Structure Generation
7.7. Future of Synthetic Data and Small Models
8. 🔄 The Cycle of Fine-Tuning and Prompt Engineering
- The AI industry initially focused on fine-tuning models like BERT for specific use cases, which proved challenging.
- Larger models led to a shift towards prompt engineering to solve tasks more efficiently.
- There is a trend back towards fine-tuning due to the high costs associated with large models, suggesting a preference for smaller, specialized models.
- The industry is expected to see more fine-tuning and less reliance on prompt engineering as a cost-effective strategy.