Multi-head attention debiasing and contrastive learning for mitigating Dataset Artifacts in Natural Language Inference

• URL: http://arxiv.org/abs/2412.16194v1
• Date: Mon, 16 Dec 2024 17:12:21 GMT
• Title: Multi-head attention debiasing and contrastive learning for mitigating Dataset Artifacts in Natural Language Inference
• Authors: Karthik Sivakoti,
• Abstract summary: We develop a novel structural debiasing approach for Natural Language Inference models. Our approach reduces the error rate from 14.19% to 10.42% while maintaining high performance on unbiased examples.
• Score: 0.0
• License:
• Abstract: While Natural Language Inference (NLI) models have achieved high performances on benchmark datasets, there are still concerns whether they truly capture the intended task, or largely exploit dataset artifacts. Through detailed analysis of the Stanford Natural Language Inference (SNLI) dataset, we have uncovered complex patterns of various types of artifacts and their interactions, leading to the development of our novel structural debiasing approach. Our fine-grained analysis of 9,782 validation examples reveals four major categories of artifacts: length-based patterns, lexical overlap, subset relationships, and negation patterns. Our multi-head debiasing architecture achieves substantial improvements across all bias categories: length bias accuracy improved from 86.03% to 90.06%, overlap bias from 91.88% to 93.13%, subset bias from 95.43% to 96.49%, and negation bias from 88.69% to 94.64%. Overall, our approach reduces the error rate from 14.19% to 10.42% while maintaining high performance on unbiased examples. Analysis of 1,026 error cases shows significant improvement in handling neutral relationships, traditionally one of the most challenging areas for NLI systems.

Related papers

• STOP! Benchmarking Large Language Models with Sensitivity Testing on Offensive Progressions [6.19084217044276]
  Mitigating explicit and implicit biases in Large Language Models (LLMs) has become a critical focus in the field of natural language processing. We introduce the Sensitivity Testing on Offensive Progressions dataset, which includes 450 offensive progressions containing 2,700 unique sentences. Our findings reveal that even the best-performing models detect bias inconsistently, with success rates ranging from 19.3% to 69.8%.
  arXiv  Detail & Related papers  (2024-09-20T18:34:38Z)
• Exploring Factual Entailment with NLI: A News Media Study [0.9208007322096533]
  We explore the relationship between factuality and Natural Language Inference (NLI) by introducing FactRel. Our analysis shows that 84% of factually supporting pairs and 63% of undermining factually pairs do not amount to NLI entailment or contradiction. We experiment with models for pairwise classification on the new dataset, and find that in some cases, generating synthetic data with GPT-4 on the basis of the annotated dataset can improve performance.
  arXiv  Detail & Related papers  (2024-06-24T17:47:55Z)
• Common 7B Language Models Already Possess Strong Math Capabilities [61.61442513067561]
  This paper shows that the LLaMA-2 7B model with common pre-training already exhibits strong mathematical abilities. The potential for extensive scaling is constrained by the scarcity of publicly available math questions.
  arXiv  Detail & Related papers  (2024-03-07T18:00:40Z)
• Dissecting vocabulary biases datasets through statistical testing and automated data augmentation for artifact mitigation in Natural Language Inference [3.154631846975021]
  We focus on investigating dataset artifacts and developing strategies to address these issues. We propose several automatic data augmentation strategies spanning character to word levels. Experiments demonstrate that the proposed approaches effectively enhance model accuracy and reduce biases by up to 0.66% and 1.14%, respectively.
  arXiv  Detail & Related papers  (2023-12-14T08:46:26Z)
• Causality and Independence Enhancement for Biased Node Classification [56.38828085943763]
  We propose a novel Causality and Independence Enhancement (CIE) framework, applicable to various graph neural networks (GNNs) Our approach estimates causal and spurious features at the node representation level and mitigates the influence of spurious correlations. Our approach CIE not only significantly enhances the performance of GNNs but outperforms state-of-the-art debiased node classification methods.
  arXiv  Detail & Related papers  (2023-10-14T13:56:24Z)
• General Greedy De-bias Learning [163.65789778416172]
  We propose a General Greedy De-bias learning framework (GGD), which greedily trains the biased models and the base model like gradient descent in functional space. GGD can learn a more robust base model under the settings of both task-specific biased models with prior knowledge and self-ensemble biased model without prior knowledge.
  arXiv  Detail & Related papers  (2021-12-20T14:47:32Z)
• Variational Information Bottleneck for Effective Low-Resource Fine-Tuning [40.66716433803935]
  We propose to use Variational Information Bottleneck (VIB) to suppress irrelevant features when fine-tuning on low-resource target tasks. We show that our VIB model finds sentence representations that are more robust to biases in natural language inference datasets.
  arXiv  Detail & Related papers  (2021-06-10T03:08:13Z)
• Re-TACRED: Addressing Shortcomings of the TACRED Dataset [5.820381428297218]
  TACRED is one of the largest and most widely used sentence-level relation extraction datasets. Proposed models that are evaluated using this dataset consistently set new state-of-the-art performance. However, they still exhibit large error rates despite leveraging external knowledge and unsupervised pretraining on large text corpora.
  arXiv  Detail & Related papers  (2021-04-16T22:55:11Z)
• Improving Robustness by Augmenting Training Sentences with Predicate-Argument Structures [62.562760228942054]
  Existing approaches to improve robustness against dataset biases mostly focus on changing the training objective. We propose to augment the input sentences in the training data with their corresponding predicate-argument structures. We show that without targeting a specific bias, our sentence augmentation improves the robustness of transformer models against multiple biases.
  arXiv  Detail & Related papers  (2020-10-23T16:22:05Z)
• Towards Robustifying NLI Models Against Lexical Dataset Biases [94.79704960296108]
  This paper explores both data-level and model-level debiasing methods to robustify models against lexical dataset biases. First, we debias the dataset through data augmentation and enhancement, but show that the model bias cannot be fully removed via this method. The second approach employs a bag-of-words sub-model to capture the features that are likely to exploit the bias and prevents the original model from learning these biased features.
  arXiv  Detail & Related papers  (2020-05-10T17:56:10Z)
• Adversarial Filters of Dataset Biases [96.090959788952]
  Large neural models have demonstrated human-level performance on language and vision benchmarks. Their performance degrades considerably on adversarial or out-of-distribution samples. We propose AFLite, which adversarially filters such dataset biases.
  arXiv  Detail & Related papers  (2020-02-10T21:59:21Z)

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.