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Found 8 papers, 4 papers with code
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.
Synergizing Spatial Optimization with Large Language Models for Open-Domain Urban Itinerary Planning
In this paper, we for the first time propose the task of Open-domain Urban Itinerary Planning (OUIP) for citywalk, which directly generates itineraries based on users' requests described in natural language.
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.
HGARN: Hierarchical Graph Attention Recurrent Network for Human Mobility Prediction
To address these issues, we present Hierarchical Graph Attention Recurrent Network (HGARN) for human mobility prediction.
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.
