Contextually Guided Transformers via Low-Rank Adaptation

• URL: http://arxiv.org/abs/2506.05672v1
• Date: Fri, 06 Jun 2025 01:34:39 GMT
• Title: Contextually Guided Transformers via Low-Rank Adaptation
• Authors: Andrey Zhmoginov, Jihwan Lee, Max Vladymyrov, Mark Sandler,
• Abstract summary: Large Language Models (LLMs) based on Transformers excel at text processing, but their reliance on prompts for specialized behavior introduces computational overhead.<n>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.
• Score: 14.702057924366345
• License: http://creativecommons.org/licenses/by/4.0/
• 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 connections to Variational Autoencoders and promoting smoother, more consistent context encoding. This work offers a novel direction for efficient and adaptable language modeling by integrating context directly into the model's architecture.

Related papers

• Linear Transformers with Learnable Kernel Functions are Better In-Context Models [3.3865605512957453]
  We present an elegant alteration to the Based kernel that amplifies its In-Context Learning abilities. In our work, we present a singular, elegant alteration to the Based kernel that amplifies its In-Context Learning abilities evaluated with the Multi-Query Associative Recall task.
  arXiv  Detail & Related papers  (2024-02-16T12:44:15Z)
• On Conditional and Compositional Language Model Differentiable Prompting [75.76546041094436]
  Prompts have been shown to be an effective method to adapt a frozen Pretrained Language Model (PLM) to perform well on downstream tasks. We propose a new model, Prompt Production System (PRopS), which learns to transform task instructions or input metadata, into continuous prompts.
  arXiv  Detail & Related papers  (2023-07-04T02:47:42Z)
• Bayesian Prompt Learning for Image-Language Model Generalization [64.50204877434878]
  We use the regularization ability of Bayesian methods to frame prompt learning as a variational inference problem. Our approach regularizes the prompt space, reduces overfitting to the seen prompts and improves the prompt generalization on unseen prompts. We demonstrate empirically on 15 benchmarks that Bayesian prompt learning provides an appropriate coverage of the prompt space.
  arXiv  Detail & Related papers  (2022-10-05T17:05:56Z)
• Pre-Training a Graph Recurrent Network for Language Representation [34.4554387894105]
  We consider a graph recurrent network for language model pre-training, which builds a graph structure for each sequence with local token-level communications. We find that our model can generate more diverse outputs with less contextualized feature redundancy than existing attention-based models.
  arXiv  Detail & Related papers  (2022-09-08T14:12:15Z)
• Structural Biases for Improving Transformers on Translation into Morphologically Rich Languages [120.74406230847904]
  TP-Transformer augments the traditional Transformer architecture to include an additional component to represent structure. The second method imbues structure at the data level by segmenting the data with morphological tokenization. We find that each of these two approaches allows the network to achieve better performance, but this improvement is dependent on the size of the dataset.
  arXiv  Detail & Related papers  (2022-08-11T22:42:24Z)
• N-Grammer: Augmenting Transformers with latent n-grams [35.39961549040385]
  We propose a simple yet effective modification to the Transformer architecture inspired by the literature in statistical language modeling, by augmenting the model with n-grams that are constructed from a discrete latent representation of the text sequence. We evaluate our model, the N-Grammer on language modeling on the C4 data-set as well as text classification on the SuperGLUE data-set, and find that it outperforms several strong baselines such as the Transformer and the Primer.
  arXiv  Detail & Related papers  (2022-07-13T17:18:02Z)
• Transformer Grammars: Augmenting Transformer Language Models with Syntactic Inductive Biases at Scale [31.293175512404172]
  We introduce Transformer Grammars -- a class of Transformer language models that combine expressive power, scalability, and strong performance of Transformers. We find that Transformer Grammars outperform various strong baselines on multiple syntax-sensitive language modeling evaluation metrics.
  arXiv  Detail & Related papers  (2022-03-01T17:22:31Z)
• Sentence Bottleneck Autoencoders from Transformer Language Models [53.350633961266375]
  We build a sentence-level autoencoder from a pretrained, frozen transformer language model. We adapt the masked language modeling objective as a generative, denoising one, while only training a sentence bottleneck and a single-layer modified transformer decoder. We demonstrate that the sentence representations discovered by our model achieve better quality than previous methods that extract representations from pretrained transformers on text similarity tasks, style transfer, and single-sentence classification tasks in the GLUE benchmark, while using fewer parameters than large pretrained models.
  arXiv  Detail & Related papers  (2021-08-31T19:39:55Z)
• GroupBERT: Enhanced Transformer Architecture with Efficient Grouped Structures [57.46093180685175]
  We demonstrate a set of modifications to the structure of a Transformer layer, producing a more efficient architecture. We add a convolutional module to complement the self-attention module, decoupling the learning of local and global interactions. We apply the resulting architecture to language representation learning and demonstrate its superior performance compared to BERT models of different scales.
  arXiv  Detail & Related papers  (2021-06-10T15:41:53Z)
• Time-Stamped Language Model: Teaching Language Models to Understand the Flow of Events [8.655294504286635]
  We propose to formulate this task as a question answering problem. This enables us to use pre-trained language models on other QA benchmarks by adapting those to the procedural text understanding. Our model evaluated on the Propara dataset shows improvements on the published state-of-the-art results with a $3.1%$ increase in F1 score.
  arXiv  Detail & Related papers  (2021-04-15T17:50:41Z)
• GTAE: Graph-Transformer based Auto-Encoders for Linguistic-Constrained Text Style Transfer [119.70961704127157]
  Non-parallel text style transfer has attracted increasing research interests in recent years. Current approaches still lack the ability to preserve the content and even logic of original sentences. We propose a method called Graph Transformer based Auto-GTAE, which models a sentence as a linguistic graph and performs feature extraction and style transfer at the graph level.
  arXiv  Detail & Related papers  (2021-02-01T11:08:45Z)
• Parameter Efficient Multimodal Transformers for Video Representation Learning [108.8517364784009]
  This work focuses on reducing the parameters of multimodal Transformers in the context of audio-visual video representation learning. We show that our approach reduces parameters up to 80$%$, allowing us to train our model end-to-end from scratch. To demonstrate our approach, we pretrain our model on 30-second clips from Kinetics-700 and transfer it to audio-visual classification tasks.
  arXiv  Detail & Related papers  (2020-12-08T00:16:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.