Internal Data Repetition Destroys Language Models
📰 ArXiv cs.AI
Internal data repetition hurts language model performance, and removing it can lead to significant compute-equivalent gains
Action Steps
- Measure data repetition in your training corpus using tools like deduplication algorithms
- Apply a fitted no-repetition scaling law to estimate Compute-Equivalent Gain and Compute-Equivalent Loss
- Use techniques like data augmentation or active learning to reduce repetition in your training data
- Evaluate the performance of your language model with and without repetition reduction strategies
- Optimize your training pipeline to minimize data repetition and maximize compute-equivalent gains
Who Needs to Know This
NLP engineers and researchers working with large language models can benefit from understanding the impact of internal data repetition on model performance and apply strategies to mitigate it
Key Insight
💡 Internal data repetition can lead to significant performance degradation in language models, and removing it can result in substantial compute-equivalent gains
Share This
💡 Internal data repetition can destroy language models! Remove repetition to gain significant compute-equivalent benefits
Key Takeaways
Internal data repetition hurts language model performance, and removing it can lead to significant compute-equivalent gains
Full Article
Title: Internal Data Repetition Destroys Language Models
Abstract:
arXiv:2606.24998v1 Announce Type: cross Abstract: Language models are running out of high-quality training data, and even aggressively deduplicated corpora retain some amount of repetition. Earlier controlled studies predated Chinchilla-style scaling laws and could only measure the cost of repetition indirectly. We revisit repetition in the Chinchilla era, using a fitted no-repetition scaling law to report Compute-Equivalent Gain and Compute-Equivalent Loss. We show that under this modernized pa
Abstract:
arXiv:2606.24998v1 Announce Type: cross Abstract: Language models are running out of high-quality training data, and even aggressively deduplicated corpora retain some amount of repetition. Earlier controlled studies predated Chinchilla-style scaling laws and could only measure the cost of repetition indirectly. We revisit repetition in the Chinchilla era, using a fitted no-repetition scaling law to report Compute-Equivalent Gain and Compute-Equivalent Loss. We show that under this modernized pa
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