Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective (2412.12276v3)

Abstract: Autoregressive transformers exhibit adaptive learning through in-context learning (ICL), which begs the question of how. Prior works have shown that transformers represent the ICL tasks as vectors in their representations. In this paper, we leverage the encoding-decoding framework to study how transformers form task vectors during pretraining and how their task encoding quality predicts ICL task performance. On synthetic ICL tasks, we analyze the training dynamics of a small transformer and report the coupled emergence of task encoding and decoding. As the model learns to encode different latent tasks (e.g., "Finding the first noun in a sentence.") into distinct, separable representations, it concurrently builds conditional decoding algorithms and improves its ICL performance. We validate this phenomenon across pretrained models of varying scales (Gemma-2 2B/9B/27B, Llama-3.1 8B/70B) and over the course of pretraining in OLMo-7B. Further, we demonstrate that the quality of task encoding inferred from representations predicts ICL performance, and that, surprisingly, finetuning the earlier layers can improve the task encoding and performance more than finetuning the latter layers. Our empirical insights shed light into better understanding the success and failure modes of LLMs via their representations.

• The paper demonstrates a two-stage concept encoding-decoding mechanism that underpins in-context learning in transformers.
• The authors validate their mechanism using synthetic tasks and pretrained models like Llama-3.1, linking distinct subspace representations to improved task performance.
• The study identifies concept decodability as a key metric correlated with enhanced in-context learning, highlighting the importance of early layer fine-tuning.

Concept Encoding and Decoding in In-Context Learning with Transformers

The paper "Emergence of Abstractions: Concept Encoding and Decoding Mechanism for In-Context Learning in Transformers" examines how LLMs, specifically transformers, develop abstractions necessary for in-context learning (ICL). The authors propose a concept encoding-decoding mechanism to understand how transformers form abstractions in their internal representations, enabling effective ICL.

Overview of the Proposed Mechanism

In-context learning allows LLMs to adapt to new tasks without parameter updates by conditioning on a few given examples. The paper focuses on this adaptability, which relies on forming abstractions similar to how humans distill complex experiences into fundamental principles. The authors argue that transformers perform ICL by encoding latent concepts from input sequences into distinct, separable representations—a process they term "concept encoding." Concurrently, transformers learn to apply context-specific decoding algorithms to map these encoded representations onto task-specific outputs, coined as "concept decoding."

Synthetic Experiments

To investigate their hypothesis, the authors trained a small transformer model on synthetic tasks of sparse linear regression with latent bases. These experiments showed that as training progresses, the model begins to encode different bases (concepts) into distinct subspaces. Simultaneously, the model's decoding algorithms become conditioned on these subspace representations, aligning with the two-stage concept encoding-decoding process. The emergence of separable representations coincides with improved ICL performance, supporting the hypothesized mechanistic coupling between encoding and decoding.

Validation in Pretrained Models

The authors extend their analysis to pretrained models like Llama-3.1 and Gemma-2 across various scales and tasks, such as part-of-speech tagging and bitwise arithmetic. UMAP visualizations and kNN classification were employed to examine representation separability, revealing that models like Llama-3.1-8B form increasingly distinct subspaces with more in-context examples. Furthermore, the authors validate the hypothesis through mechanistic interventions, showing performance improvements or degradations by altering internal representations. These findings establish a causal link between the encoded concept representations and the subsequent application of task-specific decoding algorithms.

Predictability and Causal Importance

The predictability of ICL performance from the quality of concept encoding, measured through a proposed concept decodability (CD) metric, demonstrates consistent correlation across tasks and model scales. Higher CD scores indicate better ICL task performance, underscoring the importance of accurate concept encoding for effective learning. Interestingly, performance gains were more significant when earlier layers were finetuned, supporting the notion that those layers are crucial for encoding latent concepts.

Implications and Future Research Directions

The implications of this paper extend to understanding model behavior, particularly the conditions under which transformers succeed or fail at particular ICL tasks. By refining the representation learning in early layers, models may better learn to discern latent concepts, offering insights into improved pretraining strategies and model architectures. The encoding-decoding framework also brings interpretive clarity to how models handle conceptually overlapping or distinct tasks.

Conclusion

This research furthers the understanding of in-context learning by elucidating the interplay between concept encoding and decoding mechanisms within transformers. It provides both empirical evidence and theoretic groundwork for examining abstraction formation in LLMs, contributing to the broader field of AI interpretability and guiding the future development of more robust and adaptable models. The paper invites further investigation into how these mechanisms adapt across different tasks, encouraging exploration into more complex, real-world datasets and multi-step reasoning tasks.