Pengfei Hong

According to our database1, Pengfei Hong authored at least 14 papers between 2019 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

On csauthors.net:

Bibliography

2024
Stuck in the Quicksand of Numeracy, Far from AGI Summit: Evaluating LLMs' Mathematical Competency through Ontology-guided Perturbations.
CoRR, 2024

2023
Dialogue Relation Extraction with Document-Level Heterogeneous Graph Attention Networks.
Cogn. Comput., March, 2023

INSTRUCTEVAL: Towards Holistic Evaluation of Instruction-Tuned Large Language Models.
CoRR, 2023

Few-shot Joint Multimodal Aspect-Sentiment Analysis Based on Generative Multimodal Prompt.
CoRR, 2023

ReMask: A Robust Information-Masking Approach for Domain Counterfactual Generation.
CoRR, 2023

Multiple Contrastive Learning for Multimodal Sentiment Analysis.
Proceedings of the IEEE International Conference on Acoustics, 2023

Few-shot Joint Multimodal Aspect-Sentiment Analysis Based on Generative Multimodal Prompt.
Proceedings of the Findings of the Association for Computational Linguistics: ACL 2023, 2023

Uncertainty Guided Label Denoising for Document-level Distant Relation Extraction.
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2023

A Robust Information-Masking Approach for Domain Counterfactual Generation.
Proceedings of the Findings of the Association for Computational Linguistics: ACL 2023, 2023

2022
Few-shot Multimodal Sentiment Analysis based on Multimodal Probabilistic Fusion Prompts.
CoRR, 2022

2021
Recognizing Emotion Cause in Conversations.
Cogn. Comput., 2021

CIDER: Commonsense Inference for Dialogue Explanation and Reasoning.
Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue, 2021

2020
MIME: MIMicking Emotions for Empathetic Response Generation.
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, 2020

2019
An Ensemble Machine Learning Model For the Early Detection of Sepsis From Clinical Data.
Proceedings of the 46th Computing in Cardiology, 2019


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