Search Results for author: Yihong Tang

Found 8 papers, 4 papers with code

Enhancing Role-playing Systems through Aggressive Queries: Evaluation and Improvement

no code implementations16 Feb 2024 Yihong Tang, Jiao Ou, Che Liu, Fuzheng Zhang, Di Zhang, Kun Gai

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.

Dialogue Generation

DialogBench: Evaluating LLMs as Human-like Dialogue Systems

no code implementations3 Nov 2023 Jiao Ou, Junda Lu, Che Liu, Yihong Tang, Fuzheng Zhang, Di Zhang, Kun Gai

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.

Dialogue Evaluation

Enhancing Personalized Dialogue Generation with Contrastive Latent Variables: Combining Sparse and Dense Persona

1 code implementation19 May 2023 Yihong Tang, Bo wang, Miao Fang, Dongming Zhao, Kun Huang, Ruifang He, Yuexian Hou

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.

Dialogue Generation

HGARN: Hierarchical Graph Attention Recurrent Network for Human Mobility Prediction

1 code implementation14 Oct 2022 Yihong Tang, Junlin He, Zhan Zhao

To address these issues, we present Hierarchical Graph Attention Recurrent Network (HGARN) for human mobility prediction.

Decoder Graph Attention

Few-Sample Traffic Prediction with Graph Networks using Locale as Relational Inductive Biases

1 code implementation8 Mar 2022 Mingxi Li, Yihong Tang, Wei Ma

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.

Management Open-Ended Question Answering +1

Attacking Deep Reinforcement Learning-Based Traffic Signal Control Systems with Colluding Vehicles

no code implementations4 Nov 2021 Ao Qu, Yihong Tang, Wei Ma

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.

reinforcement-learning Reinforcement Learning (RL)

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