GASE: Generatively Augmented Sentence Encoding

• URL: http://arxiv.org/abs/2411.04914v2
• Date: Sat, 06 Sep 2025 12:32:14 GMT
• Title: GASE: Generatively Augmented Sentence Encoding
• Authors: Manuel Frank, Haithem Afli,
• Abstract summary: We propose a training-free approach to improve sentence embeddings leveraging test-time compute.<n>Generatively Augmented Sentence generatives the input text by paraphrasing, summarising, or extracting keywords.<n>We find that generative augmentation leads to larger performance improvements for embedding models with lower baseline performance.
• Score: 0.21485350418225244
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We propose a training-free approach to improve sentence embeddings leveraging test-time compute by applying generative text models for data augmentation at inference time. Unlike conventional data augmentation that utilises synthetic training data, our approach does not require access to model parameters or the computational resources typically required for fine-tuning state-of-the-art models. Generatively Augmented Sentence Encoding variates the input text by paraphrasing, summarising, or extracting keywords, followed by pooling the original and synthetic embeddings. Experimental results on the Massive Text Embedding Benchmark for Semantic Textual Similarity (STS) demonstrate performance improvements across a range of embedding models using different generative models for augmentation. We find that generative augmentation leads to larger performance improvements for embedding models with lower baseline performance. These findings suggest that integrating generative augmentation at inference time adds semantic diversity and can enhance the robustness and generalisability of sentence embeddings for embedding models. Our results show that performance gains depend on the embedding model and the dataset.

Related papers

• Nonparametric Data Attribution for Diffusion Models [57.820618036556084]
  Data attribution for generative models seeks to quantify the influence of individual training examples on model outputs.<n>We propose a nonparametric attribution method that operates entirely on data, measuring influence via patch-level similarity between generated and training images.
  arXiv  Detail & Related papers  (2025-10-16T03:37:16Z)
• Muddit: Liberating Generation Beyond Text-to-Image with a Unified Discrete Diffusion Model [118.52589065972795]
  We introduce Muddit, a unified discrete diffusion transformer that enables fast and parallel generation across both text and image modalities.<n>Unlike prior unified diffusion models trained from scratch, Muddit integrates strong visual priors from a pretrained text-to-image backbone with a lightweight text decoder.
  arXiv  Detail & Related papers  (2025-05-29T16:15:48Z)
• Boosting Statistic Learning with Synthetic Data from Pretrained Large Models [6.596689283714373]
  We propose a novel end-to-end framework that generates and systematically filters synthetic data through domain-specific statistical methods.<n>Our experiments demonstrate consistent improvements in predictive performance across various settings.
  arXiv  Detail & Related papers  (2025-05-08T06:55:22Z)
• Integrating Textual Embeddings from Contrastive Learning with Generative Recommender for Enhanced Personalization [8.466223794246261]
  We propose a hybrid framework that augments the generative recommender with contrastive text embedding model. We evaluate our method on two domains from the Amazon Reviews 2023 dataset.
  arXiv  Detail & Related papers  (2025-04-13T15:23:00Z)
• Exploring Training and Inference Scaling Laws in Generative Retrieval [50.82554729023865]
  We investigate how model size, training data scale, and inference-time compute jointly influence generative retrieval performance. Our experiments show that n-gram-based methods demonstrate strong alignment with both training and inference scaling laws. We find that LLaMA models consistently outperform T5 models, suggesting a particular advantage for larger decoder-only models in generative retrieval.
  arXiv  Detail & Related papers  (2025-03-24T17:59:03Z)
• Scalable Language Models with Posterior Inference of Latent Thought Vectors [52.63299874322121]
  Latent-Thought Language Models (LTMs) incorporate explicit latent thought vectors that follow an explicit prior model in latent space. LTMs possess additional scaling dimensions beyond traditional LLMs, yielding a structured design space. LTMs significantly outperform conventional autoregressive models and discrete diffusion models in validation perplexity and zero-shot language modeling.
  arXiv  Detail & Related papers  (2025-02-03T17:50:34Z)
• READ: Reinforcement-based Adversarial Learning for Text Classification with Limited Labeled Data [7.152603583363887]
  Pre-trained transformer models such as BERT have shown massive gains across many text classification tasks.<n>This paper proposes a method that encapsulates reinforcement learning-based text generation and semi-supervised adversarial learning approaches.<n>Our method READ, Reinforcement-based Adversarial learning, utilizes an unlabeled dataset to generate diverse synthetic text through reinforcement learning.
  arXiv  Detail & Related papers  (2025-01-14T11:39:55Z)
• Energy-Based Diffusion Language Models for Text Generation [126.23425882687195]
  Energy-based Diffusion Language Model (EDLM) is an energy-based model operating at the full sequence level for each diffusion step. Our framework offers a 1.3$times$ sampling speedup over existing diffusion models.
  arXiv  Detail & Related papers  (2024-10-28T17:25:56Z)
• Improving Discrete Diffusion Models via Structured Preferential Generation [25.784316302130875]
  This paper tackles the challenge of improving discrete diffusion models by introducing a structured forward process. Our approach biases the generative process to produce certain categories before others, resulting in a notable improvement in log-likelihood scores on the text8 dataset.
  arXiv  Detail & Related papers  (2024-05-28T07:11:30Z)
• Distribution-Aware Data Expansion with Diffusion Models [55.979857976023695]
  We propose DistDiff, a training-free data expansion framework based on the distribution-aware diffusion model. DistDiff consistently enhances accuracy across a diverse range of datasets compared to models trained solely on original data.
  arXiv  Detail & Related papers  (2024-03-11T14:07:53Z)
• Unified Generation, Reconstruction, and Representation: Generalized Diffusion with Adaptive Latent Encoding-Decoding [90.77521413857448]
  Deep generative models are anchored in three core capabilities -- generating new instances, reconstructing inputs, and learning compact representations. We introduce Generalized generative adversarial-Decoding Diffusion Probabilistic Models (EDDPMs) EDDPMs generalize the Gaussian noising-denoising in standard diffusion by introducing parameterized encoding-decoding. Experiments on text, proteins, and images demonstrate the flexibility to handle diverse data and tasks.
  arXiv  Detail & Related papers  (2024-02-29T10:08:57Z)
• Data Augmentation for Neural Machine Translation using Generative Language Model [1.5500145658862499]
  The scarcity of large parallel corpora remains the main bottleneck in Neural Machine Translation. Data augmentation is a technique that enhances the performance of data-hungry models by generating synthetic data instead of collecting new ones. We explore prompt-based data augmentation approaches that leverage large-scale language models such as ChatGPT.
  arXiv  Detail & Related papers  (2023-07-26T02:12:58Z)
• Leveraging Synthetic Targets for Machine Translation [5.302421715411791]
  We show that training models on synthetic targets outperforms training on the actual ground-truth data. We provide preliminary analysis into whether this boost in performance is linked to ease of optimization or more deterministic nature of the predictions.
  arXiv  Detail & Related papers  (2023-05-07T07:42:22Z)
• Cross-Modal Generative Augmentation for Visual Question Answering [34.9601948665926]
  This paper introduces a generative model for data augmentation by leveraging the correlations among multiple modalities. The proposed model is able to quantify the confidence of augmented data by its generative probability, and can be jointly updated with a downstream pipeline.
  arXiv  Detail & Related papers  (2021-05-11T04:51:26Z)
• SDA: Improving Text Generation with Self Data Augmentation [88.24594090105899]
  We propose to improve the standard maximum likelihood estimation (MLE) paradigm by incorporating a self-imitation-learning phase for automatic data augmentation. Unlike most existing sentence-level augmentation strategies, our method is more general and could be easily adapted to any MLE-based training procedure.
  arXiv  Detail & Related papers  (2021-01-02T01:15:57Z)
• Rethinking embedding coupling in pre-trained language models [46.11201932668366]
  We re-evaluate the standard practice of sharing weights between input and output embeddings in pre-trained language models. We show that decoupled embeddings provide increased modeling flexibility, allowing us to significantly improve the efficiency of parameter allocation. We are able to train models that achieve strong performance on the XTREME benchmark without increasing the number of parameters at the fine-tuning stage.
  arXiv  Detail & Related papers  (2020-10-24T07:43:00Z)
• Generative Data Augmentation for Commonsense Reasoning [75.26876609249197]
  G-DAUGC is a novel generative data augmentation method that aims to achieve more accurate and robust learning in the low-resource setting. G-DAUGC consistently outperforms existing data augmentation methods based on back-translation. Our analysis demonstrates that G-DAUGC produces a diverse set of fluent training examples, and that its selection and training approaches are important for performance.
  arXiv  Detail & Related papers  (2020-04-24T06:12:10Z)
• Improve Variational Autoencoder for Text Generationwith Discrete Latent Bottleneck [52.08901549360262]
  Variational autoencoders (VAEs) are essential tools in end-to-end representation learning. VAEs tend to ignore latent variables with a strong auto-regressive decoder. We propose a principled approach to enforce an implicit latent feature matching in a more compact latent space.
  arXiv  Detail & Related papers  (2020-04-22T14:41:37Z)

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.