Search Results for author:
Found 9 papers, 1 papers with code
Language Models in Dialogue: Conversational Maxims for Human-AI Interactions
Modern language models, while sophisticated, exhibit some inherent shortcomings, particularly in conversational settings.
Detectors for Safe and Reliable LLMs: Implementations, Uses, and Limitations
Large language models (LLMs) are susceptible to a variety of risks, from non-faithful output to biased and toxic generations.
Interpretable Differencing of Machine Learning Models
Understanding the differences between machine learning (ML) models is of interest in scenarios ranging from choosing amongst a set of competing models, to updating a deployed model with new training data.
AutoDOViz: Human-Centered Automation for Decision Optimization
AutoDOViz seeks to lower the barrier of entry for data scientists in problem specification for reinforcement learning problems, leverage the benefits of AutoDO algorithms for RL pipeline search and finally, create visualizations and policy insights in order to facilitate the typical interactive nature when communicating problem formulation and solution proposals between DO experts and domain experts.
On the Safety of Interpretable Machine Learning: A Maximum Deviation Approach
Interpretable and explainable machine learning has seen a recent surge of interest.
User Driven Model Adjustment via Boolean Rule Explanations
AI solutions are heavily dependant on the quality and accuracy of the input training data, however the training data may not always fully reflect the most up-to-date policy landscape or may be missing business logic.
FROTE: Feedback Rule-Driven Oversampling for Editing Models
However, in such scenarios, it may take time for sufficient training data to accumulate in order to retrain the model to reflect the new decision boundaries.
IRF: Interactive Recommendation through Dialogue
Recent research focuses beyond recommendation accuracy, towards human factors that influence the acceptance of recommendations, such as user satisfaction, trust, transparency and sense of control. We present a generic interactive recommender framework that can add interaction functionalities to non-interactive recommender systems. We take advantage of dialogue systems to interact with the user and we design a middleware layer to provide the interaction functions, such as providing explanations for the recommendations, managing users preferences learnt from dialogue, preference elicitation and refining recommendations based on learnt preferences.
An Evaluation Framework for Interactive Recommender System
Interactive recommender systems present an opportunity to engage the user in the process by allowing them to interact with the recommendations, provide feedback and impact the results in real-time.
