Hongbang Yuan
According to our database1,
Hongbang Yuan authored at least 16 papers
between 2022 and 2026.
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Bibliography
2026
What Do LLM Agents Know About Their World? Task2Quiz: A Paradigm for Studying Environment Understanding.
CoRR, January, 2026
Look Light, Think Heavy: What Multimodal Chain-of-Thought Reasoning Can and Cannot Do.
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2026
2025
Omni-Reward: Towards Generalist Omni-Modal Reward Modeling with Free-Form Preferences.
CoRR, October, 2025
CoRR, June, 2025
CoRR, June, 2025
Beyond Under-Alignment: Atomic Preference Enhanced Factuality Tuning for Large Language Models.
Proceedings of the Findings of the Association for Computational Linguistics: NAACL 2025, Albuquerque, New Mexico, USA, April 29, 2025
RAG-RewardBench: Benchmarking Reward Models in Retrieval Augmented Generation for Preference Alignment.
Proceedings of the Findings of the Association for Computational Linguistics, 2025
Towards Robust Knowledge Unlearning: An Adversarial Framework for Assessing and Improving Unlearning Robustness in Large Language Models.
Proceedings of the Thirty-Ninth AAAI Conference on Artificial Intelligence, 2025
2024
Data Intell., 2024
RAG-RewardBench: Benchmarking Reward Models in Retrieval Augmented Generation for Preference Alignment.
CoRR, 2024
Beyond Under-Alignment: Atomic Preference Enhanced Factuality Tuning for Large Language Models.
CoRR, 2024
Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024
Whispers that Shake Foundations: Analyzing and Mitigating False Premise Hallucinations in Large Language Models.
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024
Cutting Off the Head Ends the Conflict: A Mechanism for Interpreting and Mitigating Knowledge Conflicts in Language Models.
Proceedings of the Findings of the Association for Computational Linguistics, 2024
2022
CogKTR: A Knowledge-Enhanced Text Representation Toolkit for Natural Language Understanding.
Proceedings of the The 2022 Conference on Empirical Methods in Natural Language Processing, 2022
CogKGE: A Knowledge Graph Embedding Toolkit and Benchmark for Representing Multi-source and Heterogeneous Knowledge.
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, 2022