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Found 9 papers, 0 papers with code
Adapting LLMs for Efficient, Personalized Information Retrieval: Methods and Implications
The advent of Large Language Models (LLMs) heralds a pivotal shift in online user interactions with information.
A Personalized Reinforcement Learning Summarization Service for Learning Structure from Unstructured Data
The exponential growth of textual data has created a crucial need for tools that assist users in extracting meaningful insights.
Adaptive Summaries: A Personalized Concept-based Summarization Approach by Learning from Users' Feedback
Exploring the tremendous amount of data efficiently to make a decision, similar to answering a complicated question, is challenging with many real-world application scenarios.
Am I Rare? An Intelligent Summarization Approach for Identifying Hidden Anomalies
The experimental results on benchmark datasets prove a summary of the data can be a substitute for original data in the anomaly detection task.
Are We On The Same Page? Hierarchical Explanation Generation for Planning Tasks in Human-Robot Teaming using Reinforcement Learning
In this work, we argue that the agent-generated explanations, especially the complex ones, should be abstracted to be aligned with the level of details the human teammate desires to maintain the recipient's cognitive load.
Order Matters: Generating Progressive Explanations for Planning Tasks in Human-Robot Teaming
Given this sequential process, a formulation based on goal-based MDP for generating progressive explanations is presented.
Online Explanation Generation for Human-Robot Teaming
The new explanation generation methods are based on a model reconciliation setting introduced in our prior work.
Progressive Explanation Generation for Human-robot Teaming
A progressive explanation improves understanding by limiting the cognitive effort required at each step of making the explanation.
Interactive Plan Explicability in Human-Robot Teaming
Being aware of the human teammates' expectation leads to robot behaviors that better align with human expectation, thus facilitating more efficient and potentially safer teams.
