Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
144 tokens/sec
GPT-4o
8 tokens/sec
Gemini 2.5 Pro Pro
46 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

In-context Learning and Gradient Descent Revisited (2311.07772v4)

Published 13 Nov 2023 in cs.CL and cs.LG

Abstract: In-context learning (ICL) has shown impressive results in few-shot learning tasks, yet its underlying mechanism is still not fully understood. A recent line of work suggests that ICL performs gradient descent (GD)-based optimization implicitly. While appealing, much of the research focuses on simplified settings, where the parameters of a shallow model are optimized. In this work, we revisit evidence for ICL-GD correspondence on realistic NLP tasks and models. We find gaps in evaluation, both in terms of problematic metrics and insufficient baselines. We show that surprisingly, even untrained models achieve comparable ICL-GD similarity scores despite not exhibiting ICL. Next, we explore a major discrepancy in the flow of information throughout the model between ICL and GD, which we term Layer Causality. We propose a simple GD-based optimization procedure that respects layer causality, and show it improves similarity scores significantly.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (27)
  1. Transformers learn to implement preconditioned gradient descent for in-context learning.
  2. What learning algorithm is in-context learning? investigations with linear models.
  3. Greedy layer-wise training of deep networks. Advances in neural information processing systems, 19.
  4. Language models are few-shot learners. In Advances in Neural Information Processing Systems, volume 33, pages 1877–1901. Curran Associates, Inc.
  5. Transformers implement functional gradient descent to learn non-linear functions in context.
  6. Why can gpt learn in-context? language models implicitly perform gradient descent as meta-optimizers.
  7. Analyzing transformers in embedding space.
  8. The commitmentbank: Investigating projection in naturally occurring discourse.
  9. Jump to conclusions: Short-cutting transformers with linear transformations.
  10. A mathematical framework for transformer circuits. Transformer Circuits Thread. Https://transformer-circuits.pub/2021/framework/index.html.
  11. Transformer feed-forward layers build predictions by promoting concepts in the vocabulary space.
  12. In-context learning creates task vectors.
  13. Risks from learned optimization in advanced machine learning systems.
  14. nostalgebraist. 2020. interpreting gpt: the logit lens. https://www.lesswrong.com/posts/AcKRB8wDpdaN6v6ru/interpreting-gpt-the-logit-lens.
  15. In-context learning and induction heads. ArXiv, abs/2209.11895.
  16. A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics, ACL ’04, page 271–es, USA. Association for Computational Linguistics.
  17. Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. In Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, ACL ’05, page 115–124, USA. Association for Computational Linguistics.
  18. Trainable transformer in transformer. ArXiv, abs/2307.01189.
  19. Do pretrained transformers really learn in-context by gradient descent?
  20. Recursive deep models for semantic compositionality over a sentiment treebank. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pages 1631–1642, Seattle, Washington, USA. Association for Computational Linguistics.
  21. Branchynet: Fast inference via early exiting from deep neural networks.
  22. Function vectors in large language models.
  23. Transformers learn in-context by gradient descent. In Proceedings of the 40th International Conference on Machine Learning, volume 202 of Proceedings of Machine Learning Research, pages 35151–35174. PMLR.
  24. Uncovering mesa-optimization algorithms in transformers.
  25. Emergent abilities of large language models.
  26. An explanation of in-context learning as implicit bayesian inference.
  27. Character-level convolutional networks for text classification. In Advances in Neural Information Processing Systems, volume 28. Curran Associates, Inc.
Citations (7)
View on Semantic Scholar

Summary

  • The paper reassesses the ICL-GD correspondence by showing that untrained models can achieve similar metrics and introduces Layer-Causal Gradient Descent to refine alignment.
  • The study uses a rigorous experimental framework across six NLP datasets with novel similarity metrics to challenge conventional gradient descent evaluations.
  • The proposed LCGD method yields improved attention map and hidden state update similarity, indicating enhanced potential for few-shot learning and model adaptation.

An Analysis of "In-context Learning and Gradient Descent Revisited"

Gilad Deutch, Nadav Magar, Tomer Bar Natan, and Guy Dar present a critical evaluation of the connections between in-context learning (ICL) and gradient descent (GD) within NLP. The paper explores the mechanisms of ICL, which has shown noteworthy performance in few-shot learning tasks, and challenges the strong hypothesis that ICL inherently employs a GD-like procedure. The authors explore several facets of this hypothesis through a thorough experimental approach, using realistic NLP tasks and investigating the structural aspects of transformers.

Key Contributions

The paper offers two main contributions: a reassessment of previous ICL-GD correspondence assumptions and an innovative proposal of a new GD variant, Layer-Causal Gradient Descent (LCGD), which respects information flow discrepancies, identified as "Layer Causality."

  1. Revalidation of ICL-GD Correspondence: The authors critique the work of Dai et al. (2023), discussing the metrics used to evaluate ICL and GD similarity and the baseline models implemented. Highlighting that untrained models achieve comparable ICL-GD similarity scores, they posit that strong ICL-GD correlation claims may be overstated.
  2. Layer-Causal Gradient Descent Proposal: Addressing discrepancies termed "Layer Causality," the authors propose LCGD, a variant of GD, as a more aligned methodological approach that better suits the natural, layer-by-layer information flow found in ICL processes. They show empirically that LCGD achieves higher similarity between ICL and GD, especially in terms of attention map similarity and hidden state updates.

Experimental Analysis

Using six established datasets for diverse NLP tasks, the authors conduct an intricate comparative analysis between trained and untrained models, employing both traditional and newly proposed similarity metrics. They introduce adjusted metrics—SimAOU and SimAM variants—that offer a nuanced perspective on the ICL-GD relationship by focusing on changes rather than magnitudes.

The experimental results are robust, showing little evidence for a strong ICL-GD correspondence. Notably, LCGD outshines vanilla GD in similarity metrics, suggesting it can capture a dimension of ICL that aligns with gradient updates. However, scores remain low, suggesting persistent inherent challenges with the strong correspondence hypothesis.

Implications and Future Directions

This work underscores the nuanced nature of ICL and its distinction from standard GD processes. The authors propose critical alterations to similarity metrics and baseline choices, which have significant implications for ICL research. Their findings prompt further exploration into more generalized models of learning that could bridge the observed gaps between ICL and GD.

By proposing LCGD as a lens to reassess ICL mechanisms, the authors open avenues for future research in developing more sophisticated variants or even leveraging other optimization techniques that mirror in-context adaptability. Moreover, this encourages reevaluating benchmark datasets and extending analyses to broader model classes, which could reveal deep-seated interaction patterns orchestrated by LLMs.

Conclusion

Deutch et al.'s paper provides a critical view of ICL's relationship with GD, stimulating discourse on the underlying cognitive processes of adaptive models. Their challenge to the ICL-GD paradigm via methodological refinement and novel algorithmic propositions paves the way for a revised understanding of NLP models, urging researchers to rethink the complex dynamics of learning and adaptation inherent in state-of-the-art architectures.

File Document Download Save Streamline Icon: https://streamlinehq.com PDF File Document Download Save Streamline Icon: https://streamlinehq.com Markdown