Do pretrained Transformers Learn In-Context by Gradient Descent? (2310.08540v5)

Abstract: The emergence of In-Context Learning (ICL) in LLMs remains a remarkable phenomenon that is partially understood. To explain ICL, recent studies have created theoretical connections to Gradient Descent (GD). We ask, do such connections hold up in actual pre-trained LLMs? We highlight the limiting assumptions in prior works that make their setup considerably different from the practical setup in which LLMs are trained. For example, their experimental verification uses \emph{ICL objective} (training models explicitly for ICL), which differs from the emergent ICL in the wild. Furthermore, the theoretical hand-constructed weights used in these studies have properties that don't match those of real LLMs. We also look for evidence in real models. We observe that ICL and GD have different sensitivity to the order in which they observe demonstrations. Finally, we probe and compare the ICL vs. GD hypothesis in a natural setting. We conduct comprehensive empirical analyses on LLMs pre-trained on natural data (LLaMa-7B). Our comparisons of three performance metrics highlight the inconsistent behavior of ICL and GD as a function of various factors such as datasets, models, and the number of demonstrations. We observe that ICL and GD modify the output distribution of LLMs differently. These results indicate that \emph{the equivalence between ICL and GD remains an open hypothesis} and calls for further studies.

Revisiting the Hypothesis: Do Pretrained Transformers Learn In-Context by Gradient Descent?

The paper under examination addresses the question of whether In-Context Learning (ICL) in LLMs can be understood through the lens of Gradient Descent (GD). In-Context Learning, which enables transformer models to derive patterns from task-specific demonstrations provided as input prompts, remains a topic with foundational questions about its underlying dynamics and mechanisms. The hypothesis explored by the paper is whether the functional processes of ICL can be reduced to GD, an algorithm that is well-studied in the field of optimization.

Key Insights and Findings

1. Assumptions and Real-world Applicability: The paper critiques recent theoretical connections made between ICL and GD, emphasizing their divergence from the practical training scenarios of LLMs. The assumptions in these models, especially those involving hand-constructed weights, do not conform with the properties of real-world LLMs. This discrepancy signals that the claimed equivalence of ICL and GD is contingent upon setups that diverge significantly from the settings in which contemporary LLMs operate.
2. Order Sensitivity Analysis: A novel insight offered by the paper is the order sensitivity of ICL versus that of GD. Unlike GD, where the order of input does not affect the learning outcome, ICL exhibits a high degree of sensitivity to the sequence of demonstrations. This inherent difference provides an empirical argument against the equivalence hypothesis, as an order-stable algorithm like GD is structurally different from the order-sensitive behavior exhibited by Transformers during ICL.
3. Empirical Evaluation: The authors conducted extensive empirical studies comparing the performance and behavior of models using ICL and GD. Utilizing models like LLaMa and a variety of benchmarks, they apply metrics such as token overlap and output distribution similarity to show that GD and ICL lead to distinct output distributions and performance cannot be aligned with simplistic assumptions of equivalency.

Implications and Future Directions

The paper calls for a reassessment of the claims that current implementations of ICL equate to GD, suggesting that conclusions about an equivalence between these learning paradigms should be approached with skepticism. Instead, the paper proposes that the emergent nature of ICL in pre-trained LLMs operates within a different regime, necessitating further exploration into the architecture and capabilities of transformer models without over-reliance on traditional gradient optimization analogies.

Additional research could focus on composite models that capture nuances of ICL possibly neglected by strict GD modeling. Moreover, future studies could aim to dissect whether ICL aligns more with alternative optimization mechanisms or representational learning paradigms distinct from explicit parameter updates via GD. Understanding this could drive the development of more effective architectures that align both theoretical insights with practical model performance, adapting LLMs for increasingly complex task landscapes. The articulation between ICL and its functional machineries remains an essential step toward more intelligent models capable of generalizing across a broader spectrum of context-rich environments in artificial intelligence applications.