Yongqi Li

Affiliations:
  • Wuhan University, School of Computer Science, China


According to our database1, Yongqi Li authored at least 11 papers between 2023 and 2025.

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

Timeline

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Bibliography

2025
Aligning VLM Assistants with Personalized Situated Cognition.
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2025

Enhancing Relation Extraction via Supervised Rationale Verification and Feedback.
Proceedings of the AAAI-25, Sponsored by the Association for the Advancement of Artificial Intelligence, February 25, 2025

Strong Empowered and Aligned Weak Mastered Annotation for Weak-to-Strong Generalization.
Proceedings of the AAAI-25, Sponsored by the Association for the Advancement of Artificial Intelligence, February 25, 2025

2024
Adaption-of-Thought: Learning Question Difficulty Improves Large Language Models for Reasoning.
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

Prompting Large Language Models for Counterfactual Generation: An Empirical Study.
Proceedings of the 2024 Joint International Conference on Computational Linguistics, 2024

An Ensemble-of-Experts Framework for Rehearsal-free Continual Relation Extraction.
Proceedings of the Findings of the Association for Computational Linguistics, 2024

Episodic Memory Retrieval from LLMs: A Neuromorphic Mechanism to Generate Commonsense Counterfactuals for Relation Extraction.
Proceedings of the Findings of the Association for Computational Linguistics, 2024

2023
Large Language Models as Counterfactual Generator: Strengths and Weaknesses.
CoRR, 2023

Cold-Start Multi-hop Reasoning by Hierarchical Guidance and Self-verification.
Proceedings of the Machine Learning and Knowledge Discovery in Databases: Research Track, 2023

Generating Commonsense Counterfactuals for Stable Relation Extraction.
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

Type-Aware Decomposed Framework for Few-Shot Named Entity Recognition.
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2023, 2023


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