Parallel Structures in Pre-training Data Yield In-Context Learning
(2402.12530)Abstract
Pre-trained language models (LMs) are capable of in-context learning (ICL): they can adapt to a task with only a few examples given in the prompt without any parameter update. However, it is unclear where this capability comes from as there is a stark distribution shift between pre-training text and ICL prompts. In this work, we study what patterns of the pre-training data contribute to ICL. We find that LMs' ICL ability depends on $\textit{parallel structures}$ in the pre-training data -- pairs of phrases following similar templates in the same context window. Specifically, we detect parallel structures by checking whether training on one phrase improves prediction of the other, and conduct ablation experiments to study their effect on ICL. We show that removing parallel structures in the pre-training data reduces LMs' ICL accuracy by 51% (vs 2% from random ablation). This drop persists even when excluding common patterns such as n-gram repetitions and long-range dependency, showing the diversity and generality of parallel structures. A closer look at the detected parallel structures indicates that they cover diverse linguistic tasks and span long distances in the data.
Overview
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The paper investigates how parallel structures in pre-training data contribute to language models' in-context learning (ICL) abilities, enabling them to adapt to new tasks without specific retraining.
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It introduces an innovative approach to identify parallel structures — pairs of phrases following similar templates within the same context — and assesses their impact on ICL performance.
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A series of ablation studies show removing parallel structures from the training data results in a significant decrease in ICL accuracy, highlighting their importance for the models' learning capabilities.
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The findings suggest that incorporating or mimicking parallel structures in pre-training regimes could enhance language models' generalizability and efficiency in downstream tasks.
Exploring the Role of Parallel Structures in Pre-trained Language Models' In-Context Learning Ability
Introduction to In-Context Learning and its Mysteries
The phenomenon of in-context learning (ICL) allows pre-trained language models (LMs) to adeptly adjust to new tasks by merely referencing a few example inputs and outputs in their prompts, all without the necessity for explicit parameter updates. This ability not only underpins models' capabilities in tasks ranging from chain-of-thought reasoning to behavior steering but also raises intriguing questions about its origins within the training data. Despite this, the leap from traditional pre-training on natural language text to executing novel tasks through ICL represents a significant distribution shift, leaving the exact contributing factors of the pre-training data to ICL somewhat of a mystery.
Unpacking the Significance of Parallel Structures
This work posits that parallel structures within the pre-training data—defined as pairs of phrases following similar templates within the same context—play a critical role in the emergence of ICL. Through a meticulous examination involving the ablation of such structures, the research highlights a clear and significant impact on ICL performance, offering profound insights into the nuanced interplay between data structure and model learning capabilities.
Methodological Approach
Defining and Detecting Parallel Structures
The concept of a parallel structure is introduced as a pair of phrases in a context window that seemingly emanates from the same distribution. An innovative algorithm geared towards detecting these structures assesses the impact of training on one phrase to predict another, thereby quantifying their connection and importance through decreased prediction loss.
Ablation Studies and their Revelations
Through a series of ablation experiments that carefully removed detected parallel structures from the training data, this study meticulously quantifies the drop in ICL accuracy. The findings are striking: removing parallel structures leads to a remarkable 51% reduction in ICL accuracy. This effect persists across various LM sizes and is notably more significant than random ablation, underscoring the intrinsic link between parallel structures and the LMs' ICL abilities.
Theoretical and Practical Implications
Beyond N-gram Repetitions and Long-range Dependencies
This research extends previous understandings by demonstrating that parallel structures' role in facilitating ICL extends beyond mere n-gram repetitions or long-range dependencies. The diverse linguistic tasks and patterns covered by these structures suggest a comprehensive spectrum of "in-context tasks" pre-training on which possibly equips LMs with the generalizability needed for downstream ICL performances.
Insights into Language Model Training and Architectures
The detailed analysis of parallel structures, particularly their diversity and the distances they span, offers new perspectives on designing pre-training regimes and model architectures. This could lead to methodologies that intentionally incorporate or mimic such structures to enhance ICL outcomes.
Future Directions and Limitations
While this study marks a substantial step forward in understanding ICL's underpinnings, it also acknowledges limitations, including the model size scope and the straightforward nature of the evaluated tasks. Future explorations are encouraged to delve into larger models, more complex tasks, and the role of parallel structures in multi-modal contexts, potentially unlocking further advancements in ICL and beyond.
Conclusion
In summary, this work illuminates the pivotal role of parallel structures in pre-training data as a cornerstone for in-context learning capabilities in language models. By dissecting these structures' impact through rigorous ablation studies and analytical scrutiny, the research not only enriches our understanding of LMs' inner workings but also sets the stage for future innovations in AI research and applications.
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