Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
80 tokens/sec
GPT-4o
59 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
7 tokens/sec
GPT-4.1 Pro
50 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Parallel Structures in Pre-training Data Yield In-Context Learning (2402.12530v1)

Published 19 Feb 2024 in cs.CL, cs.AI, and cs.LG

Abstract: Pre-trained LLMs (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.

PDF HTML Abstract

Exploring the Role of Parallel Structures in Pre-trained LLMs' In-Context Learning Ability

Introduction to In-Context Learning and its Mysteries

The phenomenon of in-context learning (ICL) allows pre-trained LLMs (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 paper 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 LLM 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 paper 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 LLMs. 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.

File Document Download Save Streamline Icon: https://streamlinehq.com PDF File Document Download Save Streamline Icon: https://streamlinehq.com Markdown Bookmark Chat Bubble Oval Streamline Icon: https://streamlinehq.com Chat (Pro)
Definition Search Book Streamline Icon: https://streamlinehq.com
References (33)
  1. What learning algorithm is in-context learning? investigations with linear models. ArXiv.
  2. Language models are few-shot learners. In Advances in Neural Information Processing Systems.
  3. Data distributional properties drive emergent few-shot learning in transformers. ArXiv.
  4. Meta-learning via language model in-context tuning. In Proceedings of the Annual Meeting of the Association for Computational Linguistics.
  5. Palm: Scaling language modeling with pathways. Journal of Machine Learning Research.
  6. Why can GPT learn in-context? language models secretly perform gradient descent as meta-optimizers. In Findings of the Association for Computational Linguistics: ACL 2023.
  7. What can transformers learn in-context? a case study of simple function classes. In Advances in Neural Information Processing Systems.
  8. Aaron Gokaslan and Vanya Cohen. 2019. Openwebtext corpus.
  9. Continual pre-training of large language models: How to (re) warm your model? ArXiv.
  10. Don’t stop pretraining: Adapt language models to domains and tasks. In Proceedings of the Annual Meeting of the Association for Computational Linguistics.
  11. Understanding in-context learning via supportive pretraining data. In Proceedings of the Annual Meeting of the Association for Computational Linguistics.
  12. Continual learning of language models. ArXiv.
  13. Quantifying adaptability in pre-trained language models with 500 tasks. ArXiv.
  14. Xiaonan Li and Xipeng Qiu. 2023. Finding support examples for in-context learning. In Findings of the Association for Computational Linguistics: EMNLP 2023.
  15. TruthfulQA: Measuring how models mimic human falsehoods. In Proceedings of the Annual Meeting of the Association for Computational Linguistics.
  16. Ilya Loshchilov and Frank Hutter. 2017. Decoupled weight decay regularization. ArXiv.
  17. Are emergent abilities in large language models just in-context learning? ArXiv.
  18. One step of gradient descent is provably the optimal in-context learner with one layer of linear self-attention. ArXiv.
  19. In-context learning and induction heads. ArXiv.
  20. OpenAI. 2023. Gpt-4 technical report.
  21. Language models are unsupervised multitask learners. OpenAI blog.
  22. Pretraining task diversity and the emergence of non-bayesian in-context learning for regression. ArXiv.
  23. In-context pretraining: Language modeling beyond document boundaries. ArXiv.
  24. On the effect of pretraining corpora on in-context learning by a large-scale language model. ArXiv.
  25. Do long-range language models actually use long-range context? In Proceedings of the Conference on Empirical Methods in Natural Language Processing.
  26. Principle-driven self-alignment of language models from scratch with minimal human supervision. ArXiv.
  27. Transformers learn in-context by gradient descent. In Proceedings of the International Conference on Machine Learning.
  28. Label words are anchors: An information flow perspective for understanding in-context learning. In Proceedings of the Conference on Empirical Methods in Natural Language Processing.
  29. Emergent abilities of large language models. ArXiv.
  30. Chain-of-thought prompting elicits reasoning in large language models. In Advances in Neural Information Processing Systems.
  31. An explanation of in-context learning as implicit bayesian inference. ArXiv.
  32. Understanding in-context learning from repetitions. ArXiv.
  33. C3: Continued pretraining with contrastive weak supervision for cross language ad-hoc retrieval. In Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval.
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (5)
  1. Yanda Chen (13 papers)
  2. Chen Zhao (249 papers)
  3. Zhou Yu (206 papers)
  4. Kathleen McKeown (85 papers)
  5. He He (71 papers)
Citations (6)
View on Semantic Scholar