Confidence Regularized Masked Language Modeling using Text Length

• URL: http://arxiv.org/abs/2504.06037v2
• Date: Wed, 09 Apr 2025 02:32:58 GMT
• Title: Confidence Regularized Masked Language Modeling using Text Length
• Authors: Seunghyun Ji, Soowon Lee,
• Abstract summary: Masked language modeling is a widely used method for learning language representations, where the model predicts a randomly masked word in each input.<n>This issue becomes more pronounced when the input text is short, as the possible word distribution tends to have higher entropy, potentially causing the model to become overconfident in its predictions.<n>We propose a novel confidence regularizer that adaptively adjusts the regularization strength based on the input length. Experiments on the GLUE and SQuAD benchmarks show that our method improves both accuracy and expected calibration error.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Masked language modeling is a widely used method for learning language representations, where the model predicts a randomly masked word in each input. However, this approach typically considers only a single correct answer during training, ignoring the variety of plausible alternatives that humans might choose. This issue becomes more pronounced when the input text is short, as the possible word distribution tends to have higher entropy, potentially causing the model to become overconfident in its predictions. To mitigate this, we propose a novel confidence regularizer that adaptively adjusts the regularization strength based on the input length. Experiments on the GLUE and SQuAD benchmarks show that our method improves both accuracy and expected calibration error

Related papers

• CR-UTP: Certified Robustness against Universal Text Perturbations on Large Language Models [12.386141652094999]
  Existing certified robustness based on random smoothing has shown considerable promise in certifying the input-specific text perturbations. A naive method is to simply increase the masking ratio and the likelihood of masking attack tokens, but it leads to a significant reduction in both certified accuracy and the certified radius. We introduce a novel approach, designed to identify a superior prompt that maintains higher certified accuracy under extensive masking.
  arXiv  Detail & Related papers  (2024-06-04T01:02:22Z)
• Beyond Image-Text Matching: Verb Understanding in Multimodal Transformers Using Guided Masking [0.4543820534430524]
  This work introduces an alternative probing strategy called guided masking. The proposed approach ablates different modalities using masking and assesses the model's ability to predict the masked word with high accuracy. We show that guided masking on ViLBERT, LXMERT, UNITER, and VisualBERT can predict the correct verb with high accuracy.
  arXiv  Detail & Related papers  (2024-01-29T21:22:23Z)
• Robustifying Language Models with Test-Time Adaptation [17.96043752001886]
  Large-scale language models achieved state-of-the-art performance over a number of language tasks. They fail on adversarial language examples, which are sentences optimized to fool the language models but with similar semantic meanings for humans. We show that we can reverse many language adversarial attacks by adapting the input sentence with predictions from masked words.
  arXiv  Detail & Related papers  (2023-10-29T22:37:54Z)
• Stochastic positional embeddings improve masked image modeling [95.03491875332034]
  Masked Image Modeling (MIM) is a promising self-supervised learning approach that enables learning from unlabeled images. We propose to incorporate location uncertainty into MIM by using positional embeddings (StoP) StoP reduces overfitting to location features and guides the model toward learning features that are more robust to location uncertainties.
  arXiv  Detail & Related papers  (2023-07-31T17:59:08Z)
• On the Efficacy of Sampling Adapters [82.5941326570812]
  We propose a unified framework for understanding sampling adapters. We argue that the shift they enforce can be viewed as a trade-off between precision and recall. We find that several precision-emphasizing measures indeed indicate that sampling adapters can lead to probability distributions more aligned with the true distribution.
  arXiv  Detail & Related papers  (2023-07-07T17:59:12Z)
• Word-Level Representation From Bytes For Language Modeling [46.28198397863388]
  Sub-word tokenization is not robust to noise and difficult to generalize to new languages. We introduce a cross-attention network that builds word-level representation directly from bytes, and a sub-word level prediction based on word-level hidden states. Byte2Word is on par with the strong sub-word baseline BERT but only takes up 10% of embedding size.
  arXiv  Detail & Related papers  (2022-11-23T03:11:13Z)
• Word Order Matters when you Increase Masking [70.29624135819884]
  We study the effect of removing position encodings on the pre-training objective itself, to test whether models can reconstruct position information from co-occurrences alone. We find that the necessity of position information increases with the amount of masking, and that masked language models without position encodings are not able to reconstruct this information on the task.
  arXiv  Detail & Related papers  (2022-11-08T18:14:04Z)
• uChecker: Masked Pretrained Language Models as Unsupervised Chinese Spelling Checkers [23.343006562849126]
  We propose a framework named textbfuChecker to conduct unsupervised spelling error detection and correction. Masked pretrained language models such as BERT are introduced as the backbone model. Benefiting from the various and flexible MASKing operations, we propose a Confusionset-guided masking strategy to fine-train the masked language model.
  arXiv  Detail & Related papers  (2022-09-15T05:57:12Z)
• Typical Decoding for Natural Language Generation [76.69397802617064]
  We study why high-probability texts can be dull or repetitive. We show that typical sampling offers competitive performance in terms of quality.
  arXiv  Detail & Related papers  (2022-02-01T18:58:45Z)
• Training on Test Data with Bayesian Adaptation for Covariate Shift [96.3250517412545]
  Deep neural networks often make inaccurate predictions with unreliable uncertainty estimates. We derive a Bayesian model that provides for a well-defined relationship between unlabeled inputs under distributional shift and model parameters. We show that our method improves both accuracy and uncertainty estimation.
  arXiv  Detail & Related papers  (2021-09-27T01:09:08Z)
• Improving Pretrained Cross-Lingual Language Models via Self-Labeled Word Alignment [49.45399359826453]
  Cross-lingual language models are typically pretrained with language modeling on multilingual text or parallel sentences. We introduce denoising word alignment as a new cross-lingual pre-training task. Experimental results show that our method improves cross-lingual transferability on various datasets.
  arXiv  Detail & Related papers  (2021-06-11T13:36:01Z)
• $k$-Neighbor Based Curriculum Sampling for Sequence Prediction [22.631763991832862]
  Multi-step ahead prediction in language models is challenging due to discrepancy between training and test time processes. We propose textitNearest-Neighbor Replacement Sampling -- a curriculum learning-based method that gradually changes an initially deterministic teacher policy. We report our findings on two language modelling benchmarks and find that the proposed method further improves performance when used in conjunction with scheduled sampling.
  arXiv  Detail & Related papers  (2021-01-22T20:07:29Z)
• MASKER: Masked Keyword Regularization for Reliable Text Classification [73.90326322794803]
  We propose a fine-tuning method, coined masked keyword regularization (MASKER), that facilitates context-based prediction. MASKER regularizes the model to reconstruct the keywords from the rest of the words and make low-confidence predictions without enough context. We demonstrate that MASKER improves OOD detection and cross-domain generalization without degrading classification accuracy.
  arXiv  Detail & Related papers  (2020-12-17T04:54:16Z)
• Are Some Words Worth More than Others? [3.5598388686985354]
  We propose two new intrinsic evaluation measures within the framework of a simple word prediction task. We evaluate several commonly-used large English language models using our proposed metrics.
  arXiv  Detail & Related papers  (2020-10-12T23:12:11Z)
• Pre-training Is (Almost) All You Need: An Application to Commonsense Reasoning [61.32992639292889]
  Fine-tuning of pre-trained transformer models has become the standard approach for solving common NLP tasks. We introduce a new scoring method that casts a plausibility ranking task in a full-text format. We show that our method provides a much more stable training phase across random restarts.
  arXiv  Detail & Related papers  (2020-04-29T10:54:40Z)
• Masking as an Efficient Alternative to Finetuning for Pretrained Language Models [49.64561153284428]
  We learn selective binary masks for pretrained weights in lieu of modifying them through finetuning. In intrinsic evaluations, we show that representations computed by masked language models encode information necessary for solving downstream tasks.
  arXiv  Detail & Related papers  (2020-04-26T15:03:47Z)
• UniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Training [152.63467944568094]
  We propose to pre-train a unified language model for both autoencoding and partially autoregressive language modeling tasks. Our experiments show that the unified language models pre-trained using PMLM achieve new state-of-the-art results on a wide range of natural language understanding and generation tasks.
  arXiv  Detail & Related papers  (2020-02-28T15:28:49Z)
• Limits of Detecting Text Generated by Large-Scale Language Models [65.46403462928319]
  Some consider large-scale language models that can generate long and coherent pieces of text as dangerous, since they may be used in misinformation campaigns. Here we formulate large-scale language model output detection as a hypothesis testing problem to classify text as genuine or generated.
  arXiv  Detail & Related papers  (2020-02-09T19:53:23Z)

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.