Uncertainty-Aware Natural Language Inference with Stochastic Weight Averaging

• URL: http://arxiv.org/abs/2304.04726v1
• Date: Mon, 10 Apr 2023 17:37:23 GMT
• Title: Uncertainty-Aware Natural Language Inference with Stochastic Weight Averaging
• Authors: Aarne Talman, Hande Celikkanat, Sami Virpioja, Markus Heinonen, J\"org Tiedemann
• Abstract summary: This paper introduces Bayesian uncertainty modeling using Weight Averaging-Gaussian (SWAG) in Natural Language Understanding (NLU) tasks. We demonstrate the effectiveness of the method in terms of prediction accuracy and correlation with human annotation disagreements.
• Score: 8.752563431501502
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper introduces Bayesian uncertainty modeling using Stochastic Weight Averaging-Gaussian (SWAG) in Natural Language Understanding (NLU) tasks. We apply the approach to standard tasks in natural language inference (NLI) and demonstrate the effectiveness of the method in terms of prediction accuracy and correlation with human annotation disagreements. We argue that the uncertainty representations in SWAG better reflect subjective interpretation and the natural variation that is also present in human language understanding. The results reveal the importance of uncertainty modeling, an often neglected aspect of neural language modeling, in NLU tasks.

Related papers

• On Uncertainty In Natural Language Processing [2.5076643086429993]
  This thesis studies how uncertainty in natural language processing can be characterized from a linguistic, statistical and neural perspective. We propose a method for calibrated sampling in natural language generation based on non-exchangeable conformal prediction. Lastly, we develop an approach to quantify confidence in large black-box language models using auxiliary predictors.
  arXiv  Detail & Related papers  (2024-10-04T14:08:02Z)
• Finetuning Language Models to Emit Linguistic Expressions of Uncertainty [5.591074369497796]
  Large language models (LLMs) are increasingly employed in information-seeking and decision-making tasks. LLMs tend to generate information that conflicts with real-world facts, and their persuasive style can make these inaccuracies appear confident and convincing. In this work, we explore supervised finetuning on uncertainty-augmented predictions as a method to develop models that produce linguistic expressions of uncertainty.
  arXiv  Detail & Related papers  (2024-09-18T17:52:53Z)
• On Subjective Uncertainty Quantification and Calibration in Natural Language Generation [2.622066970118316]
  Large language models often involve the generation of free-form responses, in which case uncertainty quantification becomes challenging. This work addresses these challenges from a perspective of Bayesian decision theory. We discuss how this assumption enables principled quantification of the model's subjective uncertainty and its calibration. The proposed methods can be applied to black-box language models.
  arXiv  Detail & Related papers  (2024-06-07T18:54:40Z)
• Kernel Language Entropy: Fine-grained Uncertainty Quantification for LLMs from Semantic Similarities [79.9629927171974]
  Uncertainty in Large Language Models (LLMs) is crucial for applications where safety and reliability are important. We propose Kernel Language Entropy (KLE), a novel method for uncertainty estimation in white- and black-box LLMs.
  arXiv  Detail & Related papers  (2024-05-30T12:42:05Z)
• Decomposing Uncertainty for Large Language Models through Input Clarification Ensembling [69.83976050879318]
  In large language models (LLMs), identifying sources of uncertainty is an important step toward improving reliability, trustworthiness, and interpretability. In this paper, we introduce an uncertainty decomposition framework for LLMs, called input clarification ensembling. Our approach generates a set of clarifications for the input, feeds them into an LLM, and ensembles the corresponding predictions.
  arXiv  Detail & Related papers  (2023-11-15T05:58:35Z)
• Improving the Reliability of Large Language Models by Leveraging Uncertainty-Aware In-Context Learning [76.98542249776257]
  Large-scale language models often face the challenge of "hallucination" We introduce an uncertainty-aware in-context learning framework to empower the model to enhance or reject its output in response to uncertainty.
  arXiv  Detail & Related papers  (2023-10-07T12:06:53Z)
• KNOW How to Make Up Your Mind! Adversarially Detecting and Alleviating Inconsistencies in Natural Language Explanations [52.33256203018764]
  We leverage external knowledge bases to significantly improve on an existing adversarial attack for detecting inconsistent NLEs. We show that models with higher NLE quality do not necessarily generate fewer inconsistencies.
  arXiv  Detail & Related papers  (2023-06-05T15:51:58Z)
• Uncertainty in Natural Language Processing: Sources, Quantification, and Applications [56.130945359053776]
  We provide a comprehensive review of uncertainty-relevant works in the NLP field. We first categorize the sources of uncertainty in natural language into three types, including input, system, and output. We discuss the challenges of uncertainty estimation in NLP and discuss potential future directions.
  arXiv  Detail & Related papers  (2023-06-05T06:46:53Z)
• Integrating Uncertainty into Neural Network-based Speech Enhancement [27.868722093985006]
  Supervised masking approaches in the time-frequency domain aim to employ deep neural networks to estimate a multiplicative mask to extract clean speech. This leads to a single estimate for each input without any guarantees or measures of reliability. We study the benefits of modeling uncertainty in clean speech estimation.
  arXiv  Detail & Related papers  (2023-05-15T15:55:12Z)
• Explaining Language Models' Predictions with High-Impact Concepts [11.47612457613113]
  We propose a complete framework for extending concept-based interpretability methods to NLP. We optimize for features whose existence causes the output predictions to change substantially. Our method achieves superior results on predictive impact, usability, and faithfulness compared to the baselines.
  arXiv  Detail & Related papers  (2023-05-03T14:48:27Z)
• Learning Causal Semantic Representation for Out-of-Distribution Prediction [125.38836464226092]
  We propose a Causal Semantic Generative model (CSG) based on a causal reasoning so that the two factors are modeled separately. We show that CSG can identify the semantic factor by fitting training data, and this semantic-identification guarantees the boundedness of OOD generalization error.
  arXiv  Detail & Related papers  (2020-11-03T13:16:05Z)

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.