Contextually Guided Transformers via Low-Rank Adaptation
Andrey Zhmoginov, Jihwan Lee, Max Vladymyrov, Mark Sandler
TL;DR
This paper introduces Contextually Guided Transformers (CGT), a novel architecture that encodes context directly into model weights, eliminating prompts and enabling self-adaptation for improved language modeling efficiency and interpretability.
Contribution
The paper presents a new Transformer modification that incorporates context into weights, allowing dynamic self-specialization without prompts, and enhances interpretability of contextual representations.
Findings
Effective on synthetic in-context learning tasks
Improves language modeling benchmarks
Enhances interpretability of contextual representations
Abstract
Large Language Models (LLMs) based on Transformers excel at text processing, but their reliance on prompts for specialized behavior introduces computational overhead. We propose a modification to a Transformer architecture that eliminates the need for explicit prompts by learning to encode context into the model's weights. Our Contextually Guided Transformer (CGT) model maintains a contextual summary at each sequence position, allowing it to update the weights on the fly based on the preceding context. This approach enables the model to self-specialize, effectively creating a tailored model for processing information following a given prefix. We demonstrate the effectiveness of our method on synthetic in-context learning tasks and language modeling benchmarks. Furthermore, we introduce techniques for enhancing the interpretability of the learned contextual representations, drawing…
Peer Reviews
Decision·Submitted to ICLR 2025
The CGT introduces a unique approach to embedding context into the Transformer’s architecture. By using y-components (context summaries) to dynamically modulate the model, CGT offers an innovative solution to integrate context without the need for explicit prompting at every layer. The auxiliary loss is a thoughtful addition, penalizing large changes in the context summary to encourage smooth, interpretable, and stable evolution of the y-components. This loss term is well-conceived. CGT hold
The paper’s explanation of core concepts, especially around the auxiliary loss, low-rank transformation of y-components, and the role of x- and y-components, is highly technical but but lacks clarity. The separation of x- and y-components and the exact mechanism of context modulation are only partly clear after multiple readings and external clarifications. In particular, sections describing the technical architecture, auxiliary loss, and low-rank transformations should be simplified, with more
1.This paper introduces an innovative architecture that enhances Transformer behavior on more specialized tasks. Instead of using repetitive prompts or training an additional model—which increases computation cost and complexity—it introduces self-specialization by exploring the different potential between global context and local context of embeddings. 2. They introduce a simple regularization scheme by viewing the transformer as VAE. 3.The design of the loss function and regularization functio
1.The experiments only compare CGT with a traditional autoregressive causal Transformer as a baseline across limited datasets (types of tasks). The results would be more persuasive if other modified Transformers for specialized purposes were included in the comparison. 2.The paper finds that consecutive tokens in the context summary embedding do not behave smoothly, contrary to expectations. An explanation of why this phenomenon occurs would improve the paper.
* This paper proposed an innovative solution to an interesting problem, leveraging thoughts from various research directions, such as adapter/lora, low-rank decomposition, VAE, and hyper tuning. * The authors have performed a plethora of different experiments and analyses. These results help reveal how the method works.
* The datasets used in this work have limited information. But, to answer whether we can encode context into weight adaptation, we need tasks like reading comprehension. More specifically, in the synthetic dataset, the model only needs to encode two numbers from the context; in the linear regression dataset, the model needs to encode 16 numbers; finally, in language modeling, the benefit can be mostly captured by simply encoding the topic (there are eight in total), as partially suggested in the
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Taxonomy
TopicsTopic Modeling · Generative Adversarial Networks and Image Synthesis · Multimodal Machine Learning Applications