Digital Event Horizon
The recent study on Ettin has brought new light to the age-old debate between encoders and decoders. By using identical data, architecture, and training recipes, researchers have created both encoder-only and decoder-only models that can be compared side-by-side. This research opens doors for systematic studies of model behavior beyond just accuracy metrics and highlights the importance of training objectives in NLP development.
Encoder-only models are generally more accurate than decoder-only models in classification and retrieval tasks. Decoder-only models have proven to be superior in generation tasks. Attention mechanisms differ between encoders (bidirectional) and decoders (causal). Masked language modeling (MLM) improves encoder-only model performance, while causal language modeling (CLM) is more effective for decoder-only models. A new open-data training recipe called Ettin enables fair comparison between encoders and decoders. Cross-objective training falls short in achieving state-of-the-art performance, but fine-tuning pre-trained models with different objectives unlocks new possibilities.
The recent advancements in natural language processing (NLP) have led to the development of two prominent architectures for NLP tasks: encoders and decoders. While both have proven to be effective, there has been a long-standing debate among researchers and practitioners about which architecture is better suited for specific tasks. The context data provided sheds light on this debate by highlighting the differences between encoder-only models and decoder-only models.
According to the data, encoder-only models are generally more accurate than decoder-only models in classification and retrieval tasks. However, when it comes to generation tasks, decoder-only models have proven to be superior. This suggests that the choice of architecture depends on the specific task at hand.
One of the key differences between encoders and decoders lies in their attention mechanisms. Encoder models use bidirectional attention, allowing each token to "see" all other tokens in the sequence (fully visible), whereas decoder models use causal attention, where tokens can only "see" previous tokens to enable autoregressive generation.
The data also highlights the importance of training objectives in model development. The use of masked language modeling (MLM) as a training objective has been shown to improve the performance of encoder-only models, while the use of causal language modeling (CLM) has been more effective for decoder-only models.
To address these findings, researchers have developed a new open-data training recipe called Ettin, which enables the creation of both encoder-only and decoder-only models using identical data, architecture, and training recipes. This allows for a fair comparison between the two architectures and provides insights into their strengths and weaknesses.
The study's findings also reveal that cross-objective training falls short in achieving state-of-the-art performance for both encoders and decoders. However, by fine-tuning pre-trained models with different objectives, researchers can unlock new possibilities for model development and deployment.
In conclusion, the debate between encoders and decoders is far from over. By understanding the strengths and weaknesses of each architecture and leveraging advancements in training objectives, researchers can create more effective NLP models that tackle a wide range of tasks.
Related Information:
https://www.digitaleventhorizon.com/articles/The-Great-Debate-Encoders-vs-Decoders---A-Study-on-Model-Architecture-and-Training-Objectives-deh.shtml
https://huggingface.co/blog/ettin
Published: Wed Jul 16 08:06:50 2025 by llama3.2 3B Q4_K_M
