Researchers from Carnegie Mellon University and Google explored the concept of delaying model outputs in language models by adding fake tokens. This technique, called pause training, was found to improve performance on various tasks, including extractive question-answering and reasoning. The team also discovered the optimal number of tokens for each task and observed that decreasing inference-time tokens leads to performance degradation. Further research in this area could lead to new possibilities in delayed next-token prediction.
**In a New AI Paper, CMU and Google Researchers Redefine Language Model Outputs: How Delaying Responses with Pause Tokens Boosts Performance on QA and Reasoning Tasks**
*Researchers from Carnegie Mellon University and Google conducted a study on language model outputs and explored the strategy of adding fake tokens to delay responses. By appending a pause token to the input, they found significant improvements in various tasks.*
*The addition of pause tokens creates a wider computational channel for the AI model to utilize, potentially leading to better performance. Although it remains uncertain how this adjustment might impact real-world applications, the exploration of this technique shows promise.*
*The team conducted empirical assessments on a large-scale decoder-only model and observed substantial performance gains in tasks such as extractive question-answering, reasoning, and general understanding. For example, there was an 18% increase in the model’s exact match score on the SQuAD task.*
*However, introducing pause tokens only during the final fine-tuning showed improvements in only a small fraction of cases, suggesting that it is more effective to include them throughout the training and inference processes.*
*The researchers also experimented with different configurations, finding that appending tokens was generally superior to prepending them. They also identified an optimal number of tokens for each downstream task and discovered that reducing the number of inference-time tokens led to a graceful performance degradation.*
*Moving forward, the team suggests further exploration and development to make delays more beneficial in normal pretrained models. They believe this could open up new research directions and advancements in the field.*
*To learn more, you can read the full research paper. Credits go to the researchers involved in this project. Be sure to join our ML subreddit, Facebook community, Discord channel, and sign up for our newsletter for the latest AI research news and updates.*
**How Delaying Responses with Pause Tokens Boosts Performance – Evolve your company with AI**
*If you want to leverage AI to stay ahead and revolutionize your workflow, consider using the findings from the CMU and Google research–delaying responses with pause tokens–to boost performance on tasks such as QA and reasoning.*
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