Mingjie Zhan
According to our database1,
Mingjie Zhan
authored at least 19 papers
between 2020 and 2024.
Collaborative distances:
Collaborative distances:
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Bibliography
2024
MathCoder2: Better Math Reasoning from Continued Pretraining on Model-translated Mathematical Code.
CoRR, 2024
CoRR, 2024
ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation.
CoRR, 2024
MathGenie: Generating Synthetic Data with Question Back-translation for Enhancing Mathematical Reasoning of LLMs.
CoRR, 2024
Integrating Large Language Models into Recommendation via Mutual Augmentation and Adaptive Aggregation.
CoRR, 2024
Solving Challenging Math Word Problems Using GPT-4 Code Interpreter with Code-based Self-Verification.
Proceedings of the Twelfth International Conference on Learning Representations, 2024
Proceedings of the Twelfth International Conference on Learning Representations, 2024
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2024
MathGenie: Generating Synthetic Data with Question Back-translation for Enhancing Mathematical Reasoning of LLMs.
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2024
2023
RecRanker: Instruction Tuning Large Language Model as Ranker for Top-k Recommendation.
CoRR, 2023
CoRR, 2023
Towards Versatile and Efficient Visual Knowledge Injection into Pre-trained Language Models with Cross-Modal Adapters.
CoRR, 2023
Proceedings of the Eleventh International Conference on Learning Representations, 2023
Reconstruct Before Summarize: An Efficient Two-Step Framework for Condensing and Summarizing Meeting Transcripts.
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023
Proceedings of the Findings of the Association for Computational Linguistics: ACL 2023, 2023
2021
GroupLink: An End-to-end Multitask Method for Word Grouping and Relation Extraction in Form Understanding.
CoRR, 2021
2020
DocStruct: A Multimodal Method to Extract Hierarchy Structure in Document for General Form Understanding.
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2020, 2020