Introspective Distillation for Robust Question Answering

• URL: http://arxiv.org/abs/2111.01026v1
• Date: Mon, 1 Nov 2021 15:30:15 GMT
• Title: Introspective Distillation for Robust Question Answering
• Authors: Yulei Niu, Hanwang Zhang
• Abstract summary: Question answering (QA) models are well-known to exploit data bias, e.g., the language prior in visual QA and the position bias in reading comprehension. Recent debiasing methods achieve good out-of-distribution (OOD) generalizability with a considerable sacrifice of the in-distribution (ID) performance. We present a novel debiasing method called Introspective Distillation (IntroD) to make the best of both worlds for QA.
• Score: 70.18644911309468
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Question answering (QA) models are well-known to exploit data bias, e.g., the language prior in visual QA and the position bias in reading comprehension. Recent debiasing methods achieve good out-of-distribution (OOD) generalizability with a considerable sacrifice of the in-distribution (ID) performance. Therefore, they are only applicable in domains where the test distribution is known in advance. In this paper, we present a novel debiasing method called Introspective Distillation (IntroD) to make the best of both worlds for QA. Our key technical contribution is to blend the inductive bias of OOD and ID by introspecting whether a training sample fits in the factual ID world or the counterfactual OOD one. Experiments on visual QA datasets VQA v2, VQA-CP, and reading comprehension dataset SQuAD demonstrate that our proposed IntroD maintains the competitive OOD performance compared to other debiasing methods, while sacrificing little or even achieving better ID performance compared to the non-debiasing ones.

Related papers

• What If the Input is Expanded in OOD Detection? [77.37433624869857]
  Out-of-distribution (OOD) detection aims to identify OOD inputs from unknown classes. Various scoring functions are proposed to distinguish it from in-distribution (ID) data. We introduce a novel perspective, i.e., employing different common corruptions on the input space.
  arXiv  Detail & Related papers  (2024-10-24T06:47:28Z)
• Adaptive loose optimization for robust question answering [21.166930242285446]
  We propose a simple yet effective novel loss function with adaptive loose optimization. Our main technical contribution is to reduce the loss adaptively according to the ratio between the previous and current optimization state. Our approach enables QA methods to obtain state-of-the-art in- and out-of-distribution performance in most cases.
  arXiv  Detail & Related papers  (2023-05-06T08:09:46Z)
• Towards Robust Visual Question Answering: Making the Most of Biased Samples via Contrastive Learning [54.61762276179205]
  We propose a novel contrastive learning approach, MMBS, for building robust VQA models by Making the Most of Biased Samples. Specifically, we construct positive samples for contrastive learning by eliminating the information related to spurious correlation from the original training samples. We validate our contributions by achieving competitive performance on the OOD dataset VQA-CP v2 while preserving robust performance on the ID dataset VQA v2.
  arXiv  Detail & Related papers  (2022-10-10T11:05:21Z)
• Out-of-Distribution Detection with Hilbert-Schmidt Independence Optimization [114.43504951058796]
  Outlier detection tasks have been playing a critical role in AI safety. Deep neural network classifiers usually tend to incorrectly classify out-of-distribution (OOD) inputs into in-distribution classes with high confidence. We propose an alternative probabilistic paradigm that is both practically useful and theoretically viable for the OOD detection tasks.
  arXiv  Detail & Related papers  (2022-09-26T15:59:55Z)
• MUTANT: A Training Paradigm for Out-of-Distribution Generalization in Visual Question Answering [58.30291671877342]
  We present MUTANT, a training paradigm that exposes the model to perceptually similar, yet semantically distinct mutations of the input. MUTANT establishes a new state-of-the-art accuracy on VQA-CP with a $10.57%$ improvement.
  arXiv  Detail & Related papers  (2020-09-18T00:22:54Z)
• On the Value of Out-of-Distribution Testing: An Example of Goodhart's Law [78.10523907729642]
  VQA-CP has become the standard OOD benchmark for visual question answering. Most published methods rely on explicit knowledge of the construction of the OOD splits. We show that embarrassingly-simple methods, including one that generates answers at random, surpass the state of the art on some question types.
  arXiv  Detail & Related papers  (2020-05-19T06:45:50Z)

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.