Metadata Conditioning Accelerates Language Model Pre-training
This addresses the problem of accelerating and controlling language model pre-training for AI researchers and practitioners, offering a simple, overhead-free method with incremental improvements.
The paper tackles the challenge of efficiently learning from diverse pre-training corpora by proposing Metadata Conditioning then Cooldown (MeCo), which incorporates metadata during training and later removes it, resulting in a 1.6B model matching standard performance with 33% less data and enabling steerable outputs via metadata prompts.
The vast diversity of styles, domains, and quality levels present in language model pre-training corpora is essential in developing general model capabilities, but efficiently learning and deploying the correct behaviors exemplified in each of these heterogeneous data sources is challenging. To address this, we propose a new method, termed Metadata Conditioning then Cooldown (MeCo), to incorporate additional learning cues during pre-training. MeCo first provides metadata (e.g., URLs like www$.$wikipedia$.$org) alongside the text during training and later uses a cooldown phase with only the standard text, thereby enabling the model to function normally even without metadata. MeCo significantly accelerates pre-training across different model scales (600M to 8B parameters) and training sources (C4, RefinedWeb, and DCLM). For instance, a 1.6B language model trained with MeCo matches the downstream task performance of standard pre-training while using 33% less data. Additionally, MeCo enables us to steer language models by conditioning the inference prompt on either real or fabricated metadata that encodes the desired properties of the output: for example, prepending wikipedia$.$org to reduce harmful generations or factquizmaster$.$com (fabricated) to improve common knowledge task performance. We also demonstrate that MeCo is compatible with different types of metadata, such as model-generated topics. MeCo is remarkably simple, adds no computational overhead, and demonstrates promise in producing more capable and steerable language models.