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Found 12 papers, 6 papers with code
Dense Reward for Free in Reinforcement Learning from Human Feedback
Reinforcement Learning from Human Feedback (RLHF) has been credited as the key advance that has allowed Large Language Models (LLMs) to effectively follow instructions and produce useful assistance.
Harmonizing Global Voices: Culturally-Aware Models for Enhanced Content Moderation
We train large language models on extensive datasets of media news and articles to create culturally attuned models.
When is Off-Policy Evaluation Useful? A Data-Centric Perspective
On the one hand, it brings opportunities for safe policy improvement under high-stakes scenarios like clinical guidelines.
Optimising Human-AI Collaboration by Learning Convincing Explanations
Machine learning models are being increasingly deployed to take, or assist in taking, complicated and high-impact decisions, from quasi-autonomous vehicles to clinical decision support systems.
How to Catch an AI Liar: Lie Detection in Black-Box LLMs by Asking Unrelated Questions
Large language models (LLMs) can "lie", which we define as outputting false statements despite "knowing" the truth in a demonstrable sense.
Practical Approaches for Fair Learning with Multitype and Multivariate Sensitive Attributes
It is important to guarantee that machine learning algorithms deployed in the real world do not result in unfairness or unintended social consequences.
Synthetic Model Combination: An Instance-wise Approach to Unsupervised Ensemble Learning
Consider making a prediction over new test data without any opportunity to learn from a training set of labelled data - instead given access to a set of expert models and their predictions alongside some limited information about the dataset used to train them.
POETREE: Interpretable Policy Learning with Adaptive Decision Trees
Building models of human decision-making from observed behaviour is critical to better understand, diagnose and support real-world policies such as clinical care.
Inverse Online Learning: Understanding Non-Stationary and Reactionary Policies
Human decision making is well known to be imperfect and the ability to analyse such processes individually is crucial when attempting to aid or improve a decision-maker's ability to perform a task, e. g. to alert them to potential biases or oversights on their part.
The Medkit-Learn(ing) Environment: Medical Decision Modelling through Simulation
Understanding decision-making in clinical environments is of paramount importance if we are to bring the strengths of machine learning to ultimately improve patient outcomes.
Scalable Bayesian Inverse Reinforcement Learning
Bayesian inference over the reward presents an ideal solution to the ill-posed nature of the inverse reinforcement learning problem.
Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift
In this paper, we develop an approximate Bayesian inference scheme based on posterior regularisation, wherein unlabelled target data are used as "pseudo-labels" of model confidence that are used to regularise the model's loss on labelled source data.
