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Found 27 papers, 2 papers with code
Controlled Decoding from Language Models
We propose controlled decoding (CD), a novel off-policy reinforcement learning method to control the autoregressive generation from language models towards high reward outcomes.
Multi-Objective Reinforcement Learning
reinforcement-learning
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 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.
Improving Diversity of Demographic Representation in Large Language Models via Collective-Critiques and Self-Voting
A crucial challenge for generative large language models (LLMs) is diversity: when a user's prompt is under-specified, models may follow implicit assumptions while generating a response, which may result in homogenization of the responses, as well as certain demographic groups being under-represented or even erased from the generated responses.
Batch Calibration: Rethinking Calibration for In-Context Learning and Prompt Engineering
Prompting and in-context learning (ICL) have become efficient learning paradigms for large language models (LLMs).
Towards A Scalable Solution for Improving Multi-Group Fairness in Compositional Classification
Despite the rich literature on machine learning fairness, relatively little attention has been paid to remediating complex systems, where the final prediction is the combination of multiple classifiers and where multiple groups are present.
Let's Do a Thought Experiment: Using Counterfactuals to Improve Moral Reasoning
Language models still struggle on moral reasoning, despite their impressive performance in many other tasks.
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.
Investigating Ensemble Methods for Model Robustness Improvement of Text Classifiers
Large pre-trained language models have shown remarkable performance over the past few years.
A Human-ML Collaboration Framework for Improving Video Content Reviews
We deal with the problem of localized in-video taxonomic human annotation in the video content moderation domain, where the goal is to identify video segments that violate granular policies, e. g., community guidelines on an online video platform.
Simpson's Paradox in Recommender Fairness: Reconciling differences between per-user and aggregated evaluations
We reconcile these notions and show that the tension is due to differences in distributions of users where items are relevant, and break down the important factors of the user's recommendations.
Flexible text generation for counterfactual fairness probing
A common approach for testing fairness issues in text-based classifiers is through the use of counterfactuals: does the classifier output change if a sensitive attribute in the input is changed?
Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task Learning
This presents a multi-dimensional Pareto frontier on (1) the trade-off between group fairness and accuracy with respect to each task, as well as (2) the trade-offs across multiple tasks.
Measuring Model Fairness under Noisy Covariates: A Theoretical Perspective
In this work we study the problem of measuring the fairness of a machine learning model under noisy information.
Measuring Recommender System Effects with Simulated Users
Using this simulation framework, we can (a) isolate the effect of the recommender system from the user preferences, and (b) examine how the system performs not just on average for an "average user" but also the extreme experiences under atypical user behavior.
Measuring and Reducing Gendered Correlations in Pre-trained Models
Pre-trained models have revolutionized natural language understanding.
CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation
Experiments on real-world NLP datasets demonstrate that our method can generate more diverse and fluent adversarial texts, compared to many existing adversarial text generation approaches.
Fairness without Demographics through Adversarially Reweighted Learning
Much of the previous machine learning (ML) fairness literature assumes that protected features such as race and sex are present in the dataset, and relies upon them to mitigate fairness concerns.
Practical Compositional Fairness: Understanding Fairness in Multi-Component Recommender Systems
In addition to the theoretical results, we find on multiple datasets -- including a large-scale real-world recommender system -- that the overall system's end-to-end fairness is largely achievable by improving fairness in individual components.
Toward a better trade-off between performance and fairness with kernel-based distribution matching
As recent literature has demonstrated how classifiers often carry unintended biases toward some subgroups, deploying machine learned models to users demands careful consideration of the social consequences.
Recommending what video to watch next: a multitask ranking system
In this paper, we introduce a large scale multi-objective ranking system for recommending what video to watch next on an industrial video sharing platform.
Transfer of Machine Learning Fairness across Domains
A model trained for one setting may be picked up and used in many others, particularly as is common with pre-training and cloud APIs.
Fairness in Recommendation Ranking through Pairwise Comparisons
Recommender systems are one of the most pervasive applications of machine learning in industry, with many services using them to match users to products or information.
Putting Fairness Principles into Practice: Challenges, Metrics, and Improvements
In this paper we provide a case-study on the application of fairness in machine learning research to a production classification system, and offer new insights in how to measure and address algorithmic fairness issues.
Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts
In this work, we propose a novel multi-task learning approach, Multi-gate Mixture-of-Experts (MMoE), which explicitly learns to model task relationships from data.
Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations
How can we learn a classifier that is "fair" for a protected or sensitive group, when we do not know if the input to the classifier belongs to the protected group?
Why Are You More Engaged? Predicting Social Engagement from Word Use
We present a study to analyze how word use can predict social engagement behaviors such as replies and retweets in Twitter.
