Shortcutted Commonsense: Data Spuriousness in Deep Learning of Commonsense Reasoning (2021.emnlp-main)
Copied to clipboard
| Challenge: | a recent study has found that commonsense reasoning models are learning transferable generalizations . commonsensibility is a human capacity that has been a core challenge to Artificial Intelligence since its inception. |
| Approach: | They conduct an analysis of benchmarks that involve commonsense reasoning . they find that most datasets experimented with are problematic . commonsensence is a quintessential human capacity . |
| Outcome: | The proposed model is able to perform well on commonsense reasoning tasks . the model is not learning transferable generalizations or taking advantage of shortcuts . |
Similar Papers
Exploring Strategies for Generalizable Commonsense Reasoning with Pre-trained Models (2021.emnlp-main)
Copied to clipboard
| Challenge: | Recent work proposes lightweight updates to improve commonsense reasoning models . fine-tuning can cause models to overfit to task-specific data and forget knowledge gained during training . |
| Approach: | They propose to use lightweight models to update pre-trained language models to learn commonsense background knowledge. |
| Outcome: | The proposed models learn from commonsense reasoning datasets, but they are overfitted and limited generalized. |
Identifying and Mitigating Spurious Correlations for Improving Robustness in NLP Models (2022.findings-naacl)
Copied to clipboard
| Challenge: | Existing work identifies task-specific shortcuts via human priors or error analyses, which requires extensive expertise and efforts. |
| Approach: | They propose to automatically identify spurious correlations in NLP models at scale by using existing interpretability methods to extract tokens that significantly affect model’s decision process. |
| Outcome: | The proposed method can identify spurious correlations in NLP models at scale and mitigate these leads to more robust models in multiple applications. |
Commonsense Reasoning for Natural Language Processing (2020.acl-tutorials)
Copied to clipboard
| Challenge: | In this tutorial, we will outline the various types of commonsense knowledge and discuss techniques to gather and represent commonsence knowledge. |
| Approach: | This tutorial will provide researchers with the critical foundations and recent advances in commonsense representation and reasoning. |
| Outcome: | This tutorial will outline the various types of commonsense and discuss techniques to gather and represent commonsence knowledge while highlighting the challenges specific to this type of knowledge (e.g., reporting bias). |
Competency Problems: On Finding and Removing Artifacts in Language Data (2021.emnlp-main)
Copied to clipboard
Matt Gardner, William Merrill, Jesse Dodge, Matthew Peters, Alexis Ross, Sameer Singh, Noah A. Smith
| Challenge: | Recent work in NLP has documented dataset artifacts, bias, and spurious correlations . how to tell which features have spurious instead of legitimate correlations is typically left unspecified . |
| Approach: | They propose a class of competency problems to formalize this notion into a classification . they show that realistic datasets will increasingly deviate from competency problems . |
| Outcome: | The proposed model can be used to show that models are inappropriately affected by these less extreme biases. |
An Investigation of the (In)effectiveness of Counterfactually Augmented Data (2022.acl-long)
Copied to clipboard
| Challenge: | Pretrained language models tend to rely on spurious correlations and generalize poorly to out-of-distribution (OOD) data. |
| Approach: | They propose to use counterfactually-augmented data (CAD) to identify robust features that are invariant under distribution shift to train models for OOD generalization. |
| Outcome: | The proposed model can learn robust features that are invariant under distribution shifts, but lacks spurious correlations, and may exacerbate existing correlations. |
Cracking the Contextual Commonsense Code: Understanding Commonsense Reasoning Aptitude of Deep Contextual Representations (D19-60)
Copied to clipboard
| Challenge: | Pretrained deep contextual representations have advanced the state-of-the-art on various commonsense NLP tasks, but we lack a concrete understanding of their capabilities. |
| Approach: | They investigate BERT's ability to encode various commonsense features in its embedding space, but are still deficient in many areas. |
| Outcome: | The proposed model improves performance on a downstream commonsense reasoning task while using minimal data. |
Vera: A General-Purpose Plausibility Estimation Model for Commonsense Statements (2023.emnlp-main)
Copied to clipboard
| Challenge: | Despite the advances of language models, they still produce text that contains trivial commonsense errors. |
| Approach: | They propose a general-purpose commonsense statement verification model that learns to estimate the plausibility of declarative statements based on commonsensical knowledge. |
| Outcome: | The proposed model outperforms existing models that can be repurposed for commonsense verification, even including GPT-3.5/ChatGPT/GPT-4. |
Generative Data Augmentation for Commonsense Reasoning (2020.findings-emnlp)
Copied to clipboard
Yiben Yang, Chaitanya Malaviya, Jared Fernandez, Swabha Swayamdipta, Ronan Le Bras, Ji-Ping Wang, Chandra Bhagavatula, Yejin Choi, Doug Downey
| Challenge: | Recent advances in commonsense reasoning depend on large-scale human-authored training data. |
| Approach: | They propose a generative data augmentation technique that augments human-authored training data by using pretrained language models. |
| Outcome: | The proposed technique outperforms existing methods on commonsense reasoning benchmarks and enhances out-of-distribution generalization. |
Explain Yourself! Leveraging Language Models for Commonsense Reasoning (P19-1)
Copied to clipboard
| Challenge: | Empirical results indicate that we can effectively leverage language models for commonsense reasoning. |
| Approach: | They propose to use commonsense auto-generated explanations to train language models to generate explanations that can be used during training and inference in a commonsensense Auto-Generated Explanation framework. |
| Outcome: | Empirical results show that the proposed framework improves on the commonsenseQA task by 10%. |
Analyzing Biases to Spurious Correlations in Text Classification Tasks (2022.aacl-short)
Copied to clipboard
| Challenge: | Often these systems exceed human performance, but there is a caveat: standard benchmarks often assume that training and evaluation data are drawn independently and identically from the same underlying distribution. |
| Approach: | They propose to exploit spurious correlations in training data to exploit these correlations . they show that even when only ‘stop’ words are available, it is possible to predict the class significantly better than random. |
| Outcome: | The proposed model can predict class significantly better when only ‘stop’ words are available at the input stage, but can degrade the ability of the system to generalize well to out-of-domain data. |