Henry Peng Zou

Orcid: 0009-0003-5259-4998

According to our database1, Henry Peng Zou authored at least 14 papers between 2023 and 2024.

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

Timeline

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Bibliography

2024
Towards Data Contamination Detection for Modern Large Language Models: Limitations, Inconsistencies, and Oracle Challenges.
CoRR, 2024

PersonaGym: Evaluating Persona Agents and LLMs.
CoRR, 2024

LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing.
CoRR, 2024

Deconstructing The Ethics of Large Language Models from Long-standing Issues to New-emerging Dilemmas.
CoRR, 2024

Mixed Supervised Graph Contrastive Learning for Recommendation.
CoRR, 2024

EIVEN: Efficient Implicit Attribute Value Extraction using Multimodal LLM.
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track, 2024

Large Language Models Are Involuntary Truth-Tellers: Exploiting Fallacy Failure for Jailbreak Attacks.
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

Sequential LLM Framework for Fashion Recommendation.
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: EMNLP 2024, 2024


Do We Really Need Graph Convolution During Training? Light Post-Training Graph-ODE for Efficient Recommendation.
Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, 2024

ImplicitAVE: An Open-Source Dataset and Multimodal LLMs Benchmark for Implicit Attribute Value Extraction.
Proceedings of the Findings of the Association for Computational Linguistics, 2024

2023
CrisisMatch: Semi-Supervised Few-Shot Learning for Fine-Grained Disaster Tweet Classification.
CoRR, 2023

DeCrisisMB: Debiased Semi-Supervised Learning for Crisis Tweet Classification via Memory Bank.
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2023, 2023

JointMatch: A Unified Approach for Diverse and Collaborative Pseudo-Labeling to Semi-Supervised Text Classification.
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023


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