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Found 16 papers, 7 papers with code
Reimagining Urban Science: Scaling Causal Inference with Large Language Models
Urban causal research is essential for understanding the complex dynamics of cities and informing evidence-based policies.
Vision-to-Music Generation: A Survey
Vision-to-music Generation, including video-to-music and image-to-music tasks, is a significant branch of multimodal artificial intelligence demonstrating vast application prospects in fields such as film scoring, short video creation, and dance music synthesis.
INTENT: Trajectory Prediction Framework with Intention-Guided Contrastive Clustering
To this end, we present INTENT, an efficient intention-guided trajectory prediction model that relies solely on information contained in the road agent's trajectory.
The Rise of Darkness: Safety-Utility Trade-Offs in Role-Playing Dialogue Agents
Large Language Models (LLMs) have made remarkable advances in role-playing dialogue agents, demonstrating their utility in character simulations.
Sparkle: Mastering Basic Spatial Capabilities in Vision Language Models Elicits Generalization to Composite Spatial Reasoning
Our central hypothesis is that mastering these basic spatial capabilities can significantly enhance a model's performance on composite spatial tasks requiring advanced spatial understanding and combinatorial problem-solving, with generalized improvements in visual-spatial tasks.
RoleBreak: Character Hallucination as a Jailbreak Attack in Role-Playing Systems
Role-playing systems powered by large language models (LLMs) have become increasingly influential in emotional communication applications.
ERABAL: Enhancing Role-Playing Agents through Boundary-Aware Learning
Role-playing is an emerging application in the field of Human-Computer Interaction (HCI), primarily implemented through the alignment training of a large language model (LLM) with assigned characters.
MORPHEUS: Modeling Role from Personalized Dialogue History by Exploring and Utilizing Latent Space
Traditional PDG relies on external role data, which can be scarce and raise privacy concerns.
Enhancing Role-playing Systems through Aggressive Queries: Evaluation and Improvement
Experiments on models improved by RoleAD indicate that our adversarial dataset ameliorates this deficiency, with the improvements demonstrating a degree of generalizability in ordinary scenarios.
ITINERA: Integrating Spatial Optimization with Large Language Models for Open-domain Urban Itinerary Planning
Citywalk, a recently popular form of urban travel, requires genuine personalization and understanding of fine-grained requests compared to traditional itinerary planning.
DialogBench: Evaluating LLMs as Human-like Dialogue Systems
In this paper, we propose DialogBench, a dialogue evaluation benchmark that contains 12 dialogue tasks to probe the capabilities of LLMs as human-like dialogue systems should have.
Enhancing Personalized Dialogue Generation with Contrastive Latent Variables: Combining Sparse and Dense Persona
We design a Contrastive Latent Variable-based model (CLV) that clusters the dense persona descriptions into sparse categories, which are combined with the history query to generate personalized responses.
Activity-aware Human Mobility Prediction with Hierarchical Graph Attention Recurrent Network
Human mobility prediction is a fundamental task essential for various applications in urban planning, location-based services and intelligent transportation systems.
Few-Sample Traffic Prediction with Graph Networks using Locale as Relational Inductive Biases
Currently, most of the state-of-the-art prediction models are based on graph neural networks (GNNs), and the required training samples are proportional to the size of the traffic network.
Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting across Cities
To the best of our knowledge, we are the first to employ adversarial multi-domain adaptation for network-wide traffic forecasting problems.
Attacking Deep Reinforcement Learning-Based Traffic Signal Control Systems with Colluding Vehicles
In view of this, this paper first time formulates a novel task in which a group of vehicles can cooperatively send falsified information to "cheat" DRL-based ATCS in order to save their total travel time.
