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Found 8 papers, 1 papers with code
Hallucination Detection and Hallucination Mitigation: An Investigation
Large language models (LLMs), including ChatGPT, Bard, and Llama, have achieved remarkable successes over the last two years in a range of different applications.
Predicting Evoked Emotions in Conversations
In this work, we introduce the novel problem of Predicting Emotions in Conversations (PEC) for the next turn (n+1), given combinations of textual and/or emotion input up to turn n. We systematically approach the problem by modeling three dimensions inherently connected to evoked emotions in dialogues, including (i) sequence modeling, (ii) self-dependency modeling, and (iii) recency modeling.
Adaptive Dynamic Programming for Energy-Efficient Base Station Cell Switching
In this work, we propose an approximate dynamic programming (ADP)-based method coupled with online optimization to switch on/off the cells of base stations to reduce network power consumption while maintaining adequate Quality of Service (QoS) metrics.
Conversation Derailment Forecasting with Graph Convolutional Networks
Online conversations are particularly susceptible to derailment, which can manifest itself in the form of toxic communication patterns like disrespectful comments or verbal abuse.
Policy Reuse for Communication Load Balancing in Unseen Traffic Scenarios
When deploying to an unknown traffic scenario, we select a policy from the policy bank based on the similarity between the previous-day traffic of the current scenario and the traffic observed during training.
ModelLight: Model-Based Meta-Reinforcement Learning for Traffic Signal Control
Traffic signal control is of critical importance for the effective use of transportation infrastructures.
Multi-batch Reinforcement Learning via Sample Transfer and Imitation Learning
Unfortunately, most online reinforcement learning algorithms require a large number of interactions with the environment to learn a reliable control policy.
Learning Intuitive Physics with Multimodal Generative Models
Predicting the future interaction of objects when they come into contact with their environment is key for autonomous agents to take intelligent and anticipatory actions.
