Think before you speak: Training Language Models With Pause Tokens
Training language models with pause tokens improves SQuAD task accuracy by 18%.
Key Findings
Methodology
The paper introduces a method of training language models by inserting learnable pause tokens. This allows the model to perform additional computations before generating the next token. Specifically, a sequence of pause tokens is appended to the input prefix, and the model's output is extracted only after the last pause token is seen.
Key Results
- On the SQuAD task, the 1B parameter model's EM score improved by 18%.
- On the CommonSenseQA task, accuracy improved by 8%.
- On the GSM8k reasoning task, accuracy improved by 1%.
Significance
This study demonstrates that introducing inference delays during both pretraining and fine-tuning can significantly enhance language model performance across multiple downstream tasks. This approach offers a new perspective on training and inference in language models, potentially influencing future model designs.
Technical Contribution
The technical contribution lies in proposing a new training paradigm that delays next-token generation using pause tokens. This method provides a wider computational pathway compared to existing end-to-end training methods, thereby improving model performance.
Novelty
This is the first study to introduce pause tokens to delay output in language models. Compared to traditional methods, this approach allows the model to perform more computations before generating output, enhancing performance.
Limitations
- Pause tokens may lead to performance degradation in some tasks, especially when introduced only during fine-tuning.
- The model is not robust to changes in the number of inference-time pause tokens.
Future Work
Future research could explore reducing the number of inference-time pause tokens without affecting performance and applying this method to other types of models.
AI Executive Summary
Language models typically generate responses by immediately outputting the next token, which limits their computational capacity. This paper proposes a new training method by inserting pause tokens to delay output, allowing the model to perform more computations. Experiments show that this method significantly improves model performance across multiple downstream tasks, particularly in SQuAD and CommonSenseQA.
The core of this method involves appending a sequence of learnable pause tokens to the input prefix, delaying output extraction until the last pause token is seen. This allows the model to perform additional computations before generating the next token, improving the accuracy and quality of the output.
While this method performs well in many tasks, it may lead to performance degradation in some cases, especially when pause tokens are introduced only during fine-tuning. Future research could explore reducing the number of inference-time pause tokens without affecting performance and applying this method to other types of models.
Deep Analysis
Background
In recent years, Transformer-based language models have made significant progress in natural language processing. However, these models typically generate responses by immediately outputting the next token, limiting their computational capacity. To overcome this limitation, researchers have begun exploring methods to improve model performance by delaying output.
Core Problem
Traditional language models are limited by the number of computations they can perform when generating the next token. This limitation may prevent models from fully utilizing contextual information in complex tasks, affecting performance.
Innovation
The innovation of this paper lies in inserting learnable pause tokens to delay model output. This allows the model to perform additional computations before generating the next token, improving the accuracy and quality of the output.
Methodology
- �� Append learnable pause tokens to the input prefix.
- �� Use pause tokens during both training and inference.
- �� Extract model output only after the last pause token is seen.
Experiments
Experiments were conducted on decoder-only models with 1B and 130M parameters, using the C4 dataset for causal pretraining and fine-tuning on multiple downstream tasks. Key hyperparameters include the number and position of pause tokens.
Results
On the SQuAD task, the 1B parameter model's EM score improved by 18%. On the CommonSenseQA task, accuracy improved by 8%. On the GSM8k reasoning task, accuracy improved by 1%.
Applications
This method can be directly applied to natural language processing tasks requiring high precision and complex reasoning, such as question-answering systems and reasoning tasks.
Limitations & Outlook
Pause tokens may lead to performance degradation in some tasks, especially when introduced only during fine-tuning. Additionally, the model is not robust to changes in the number of inference-time pause tokens.
Plain Language Accessible to non-experts
Imagine a factory assembly line where each worker is responsible for one step. Traditional models are like workers who start working immediately upon receiving a task, while our model is like workers who discuss and plan before starting, ensuring each step is done optimally. This approach gives the model more time to think, improving the quality of the final product.
ELI14 Explained like you're 14
Imagine you're playing a fast-paced game. Usually, you'd react immediately upon seeing an enemy, but sometimes, pausing to think about your next move can make you perform better in the game. Our research is like giving the model a pause button, allowing it to think more before making a decision, thus improving its performance!
Glossary
Pause Token
A special token used to delay model output, allowing for additional computation.
Used during both training and inference in this paper to improve model performance.
Causal Pretraining
A training method where the model predicts the next token based on previously seen tokens.
Used for the initial training phase of the model in this paper.
Decoder Model
A type of language model architecture typically used for generation tasks.
The type of model used in this paper.
EM Score
An evaluation metric that measures the exact match between model output and reference answers.
Used to evaluate performance on the SQuAD task.
CommonSenseQA
A commonsense question-answering dataset used to evaluate a model's reasoning ability.
One of the downstream tasks used to test model performance in this paper.
Open Questions Unanswered questions from this research
- 1 How to reduce the number of inference-time pause tokens without affecting performance?
- 2 How to apply the pause token method to other types of models?
Applications
Immediate Applications
Question-Answering Systems
By introducing pause tokens, improve the accuracy and response quality of question-answering systems.
Long-term Vision
Intelligent Dialogue Systems
In future intelligent dialogue systems, the pause token method can be used to enhance reasoning capabilities and user experience.
Abstract
Language models generate responses by producing a series of tokens in immediate succession: the $(K+1)^{th}$ token is an outcome of manipulating $K$ hidden vectors per layer, one vector per preceding token. What if instead we were to let the model manipulate say, $K+10$ hidden vectors, before it outputs the $(K+1)^{th}$ token? We operationalize this idea by performing training and inference on language models with a (learnable) $\textit{pause}$ token, a sequence of which is appended to the input prefix. We then delay extracting the model's outputs until the last pause token is seen, thereby allowing the model to process extra computation before committing to an answer. We empirically evaluate $\textit{pause-training}$ on decoder-only models of 1B and 130M parameters with causal pretraining on C4, and on downstream tasks covering reasoning, question-answering, general understanding and fact recall. Our main finding is that inference-time delays show gains when the model is both pre-trained and finetuned with delays. For the 1B model, we witness gains on 8 of 9 tasks, most prominently, a gain of $18\%$ EM score on the QA task of SQuAD, $8\%$ on CommonSenseQA and $1\%$ accuracy on the reasoning task of GSM8k. Our work raises a range of conceptual and practical future research questions on making delayed next-token prediction a widely applicable new paradigm.