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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.

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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.

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Authors (4)
  1. Gilad Deutch (2 papers)
  2. Nadav Magar (2 papers)
  3. Tomer Bar Natan (1 paper)
  4. Guy Dar (4 papers)
Citations (7)
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