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Found 13 papers, 0 papers with code
Can We Improve Model Robustness through Secondary Attribute Counterfactuals?
By implementing RDI in the context of toxicity detection, we find that accounting for secondary attributes can significantly improve robustness, with improvements in sliced accuracy on the original dataset up to 7% compared to existing robustness methods.
Reuse Your Rewards: Reward Model Transfer for Zero-Shot Cross-Lingual Alignment
In this work, we evaluate a simple approach for zero-shot cross-lingual alignment, where a reward model is trained on preference data in one source language and directly applied to other target languages.
Improving Few-shot Generalization of Safety Classifiers via Data Augmented Parameter-Efficient Fine-Tuning
As large language models (LLMs) are widely adopted, new safety issues and policies emerge, to which existing safety classifiers do not generalize well.
Break it, Imitate it, Fix it: Robustness by Generating Human-Like Attacks
We demonstrate the advantages of this system on the ANLI and hate speech detection benchmark datasets - both collected via an iterative, adversarial human-and-model-in-the-loop procedure.
Improving Classifier Robustness through Active Generation of Pairwise Counterfactuals
We demonstrate that with a small amount of human-annotated counterfactual data (10%), we can generate a counterfactual augmentation dataset with learned labels, that provides an 18-20% improvement in robustness and a 14-21% reduction in errors on 6 out-of-domain datasets, comparable to that of a fully human-annotated counterfactual dataset for both sentiment classification and question paraphrase tasks.
Fine-grained prediction of food insecurity using news streams
Anticipating the outbreak of a food crisis is crucial to efficiently allocate emergency relief and reduce human suffering.
Learning Faithful Representations of Causal Graphs
By incorporating these faithfulness properties, we learn text embeddings that are 31. 3{\%} more faithful to human validated causal graphs with about 800K and 200K causal links and achieve 21. 1{\%} better Precision-Recall AUC in a link prediction fine-tuning task.
Beyond The Text: Analysis of Privacy Statements through Syntactic and Semantic Role Labeling
This paper formulates a new task of extracting privacy parameters from a privacy policy, through the lens of Contextual Integrity, an established social theory framework for reasoning about privacy norms.
Identifying Predictive Causal Factors from News Streams
We propose a new framework to uncover the relationship between news events and real world phenomena.
What is Fair? Exploring Pareto-Efficiency for Fairness Constrained Classifiers
The potential for learned models to amplify existing societal biases has been broadly recognized.
Fairness Sample Complexity and the Case for Human Intervention
We demonstrate that for a classifier to approach a definition of fairness in terms of specific sensitive variables, adequate subgroup population samples need to exist and the model dimensionality has to be aligned with subgroup population distributions.
RECIPE: Applying Open Domain Question Answering to Privacy Policies
We describe our experiences in using an open domain question answering model (Chen et al., 2017) to evaluate an out-of-domain QA task of assisting in analyzing privacy policies of companies.
Unsupervised Word Influencer Networks from News Streams
We present Word Influencer Networks (WIN), a graph framework to extract longitudinal temporal relationships between any pair of informative words from news streams.
Relationship Extraction (Distant Supervised)
Stock Price Prediction
