Summary Pretraining Data Mixtures for Transformer Models arxiv.org
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One Line
The paper explores how transformer models can effectively adapt to new tasks by leveraging pretraining data.
Slides
Slide Presentation (12 slides)
Key Points
- Transformer models have the ability to perform in-context learning (ICL) and learn new tasks without explicit training.
- Pretraining data plays a crucial role in enabling the few-shot learning capabilities of transformer models.
- Transformers demonstrate near-optimal unsupervised model selection capabilities when the task families are well-represented in their pretraining data.
- However, transformers exhibit various failure modes and a degradation of generalization when presented with tasks outside of their pretraining data.
- Transformers can learn high-dimensional and non-linear functions from in-context examples.
- The authors study the model's in-context learning behavior on functions that are outside the pretraining data distribution.
- The model can perform model selection among pretrained function classes during in-context learning at little extra statistical cost.
- The in-context learning behavior of transformers does not generalize well beyond the pretraining data.
Summaries
20 word summary
This paper examines how transformer models can learn new tasks by bridging the gap between pretraining data and new tasks.
133 word summary
This paper explores the ability of transformer models to learn new tasks in-context by bridging the gap between their pretraining data mixture and new tasks. Transformers exhibit near-optimal unsupervised model selection capabilities when task families are well-represented in their pretraining data. However, they also observe failure modes and degradation of generalization when presented with tasks outside of their pretraining data. The authors highlight the few-shot learning capabilities of large language models and emphasize the importance of the pretraining process. They investigate how the composition of pretraining data affects the few-shot learning abilities of transformers. Models pretrained on a mixture of linear functions and sinusoids perform similarly to models pretrained on only one function class, but deviate from those pretrained on specific function classes when presented with functions outside any single component function class.
168 word summary
This paper explores the ability of transformer models to learn new tasks in-context by bridging the gap between their pretraining data mixture and new tasks. The authors find that transformers exhibit near-optimal unsupervised model selection capabilities when the task families are well-represented in their pretraining data. However, they also observe failure modes and a degradation of generalization when presented with tasks outside of their pretraining data. The authors highlight the few-shot learning capabilities of large language models and emphasize the importance of the pretraining process in enabling this capability. They investigate how the composition of the pretraining data affects the few-shot learning abilities of transformers. The authors conduct experiments to investigate the model selection behavior of transformers when presented with in-context examples from different function classes. They find that models pretrained on a mixture of linear functions and sinusoids perform similarly to models pretrained on only one function class, but deviate from those pretrained on specific function classes when presented with functions outside any single component function class.
430 word summary
This paper explores the ability of transformer models to learn new tasks in-context by bridging the gap between their pretraining data mixture and new tasks. The authors focus on transformers trained on (x, f(x)) pairs rather than natural language. They find that transformers exhibit near-optimal unsupervised model selection capabilities when the task families are well-represented in their pretraining data. However, they also observe various failure modes and a degradation of generalization when presented with tasks outside of their pretraining data.
The authors highlight the impressive few-shot learning capabilities of large language models, which can learn tasks in-context by providing examples and generating responses. They emphasize the importance of the pretraining process in enabling this capability and investigate how the composition of the pretraining data affects the few-shot learning abilities of transformers.
They adopt a few-shot learning setup where a small set of inputs and labels are provided to make predictions about a new input. Transformers are capable of learning various data distributions for (x, f(x)) pairs, and the test input is treated as the final element of the sequence. The authors train transformer models for in-context learning using a mixture of multiple distinct function classes and study their behavior on functions outside the pretraining data distribution. They find that the model can perform model selection among pretrained function classes during in-context learning, but its behavior does not generalize well beyond the pretraining data.
The authors provide an overview of transformers as sequence models for next-token predictions based on previous sequence tokens. They describe the data-generating model used in the study, where covariates are drawn from a normal distribution and functions are sampled from a distribution over function classes. They frame the in-context learning problem as providing a prompt sequence to the model and generating a prediction for the next token.
Previous work has demonstrated transformers' abilities to perform in-context learning on linear functions, decision trees, and ReLU networks. The authors discuss studies on generalization properties of transformers and the role of pretraining function diversity for in-context learning. They describe the training process for their model, which involves fitting the model on sequences of (x, f(x)) pairs drawn from different function classes.
In their experiments, the authors investigate the model selection behavior of transformers when presented with in-context examples from different function classes. They find that models pretrained on a mixture of linear functions and sinusoids perform similarly to models pretrained on only one function class. However, when presented with functions outside any single component function class, the model's predictions deviate from those made by models pretrained on specific function classes.
664 word summary
Pretraining Data Mixtures for Transformer Models: Summary
Transformer models, particularly large language models (LLMs), have the ability to perform in-context learning (ICL), which allows them to learn new tasks without explicit training. In this paper, the authors investigate how effectively transformers can bridge between their pretraining data mixture to identify and learn new tasks in-context. They focus on transformer models trained on sequences of (x, f(x)) pairs rather than natural language. The results show that transformers demonstrate near-optimal unsupervised model selection capabilities when the task families are well-represented in their pretraining data. However, when presented with tasks outside of their pretraining data, transformers exhibit various failure modes and a degradation of generalization.
The authors start by discussing the impressive few-shot learning capabilities of large language models, which can perform tasks in-context by providing examples and generating responses. They highlight previous work that demonstrates the ability of transformers to learn high-dimensional and non-linear functions from in-context examples. The pretraining process plays a crucial role in enabling this capability, and the authors focus on how the composition of the pretraining data affects the few-shot learning abilities of transformers.
The study adopts a few-shot learning setup where a set of inputs and labels are provided to make predictions about a new input. The number of examples is small compared to the amount of data used for pretraining. To apply sequence models for few-shot learning, the examples are passed sequentially, alternating between inputs and labels. The test input is treated as the final element of the sequence, and the model's prediction for the next item is considered as the predicted label. Previous work has shown that transformers are capable of learning various data distributions for (x, f(x)) pairs.
The authors train transformer models for in-context learning using a mixture of multiple distinct function classes. They study the model's in-context learning behavior on functions that are outside the pretraining data distribution. The results indicate that the model can perform model selection among pretrained function classes during in-context learning at little extra statistical cost. However, the model's in-context learning behavior does not generalize well beyond the pretraining data.
In the preliminaries section, the authors provide an overview of transformers as sequence models that provide next-token predictions conditional on previous sequence tokens. They describe the data-generating model used in the study, where covariates are drawn from a normal distribution and functions are sampled from a distribution over function classes. They frame the in-context learning problem as providing a single prompt sequence to the model and generating a prediction for the next token. The performance of an in-context learner is evaluated based on its predictive squared-loss.
The authors discuss previous work that demonstrates transformers' abilities to perform in-context learning on linear functions, decision trees, and ReLU networks. They also mention studies on generalization properties of transformers and the role of pretraining function diversity for in-context learning. The closest work to their study is one that explores transformers' abilities to perform model selection and provides theoretical guarantees for transformers' generalization properties.
The authors describe the training process for their model, which involves fitting the model on sequences of (x, f(x)) pairs drawn from different function classes. They present the data generation process for each function class, including dense linear functions, sparse linear functions, two-layer ReLU networks, and sinusoidal functions. They explain how the model is pretrained using a mixture of these function classes and define the normalization of each function class.
In their experiments, the authors investigate the model selection behavior of transformers when presented with in-context examples from different function classes. They find that the model pretrained on a mixture of linear functions and sinusoids performs similarly to models pretrained on only one function class. The in-context learning behavior is relatively uniform with respect to the number of in-context examples provided. However, when presented with functions that are not part of any single component function class, the model's predictions deviate from those made by models pretrained on specific function classes.
The authors