Jianshu Zhang

Orcid: 0009-0002-6584-2551

Affiliations:
  • Northwestern University, Evanston, IL, USA
  • Wuhan University, School of Cyber Science and Engineering, Wuhan, China (2021-2025)


According to our database1, Jianshu Zhang authored at least 11 papers between 2023 and 2025.

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

Timeline

Legend:

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Bibliography

2025
VLM<sup>2</sup>-Bench: A Closer Look at How Well VLMs Implicitly Link Explicit Matching Visual Cues.
CoRR, February, 2025

Personalized Visual Instruction Tuning.
Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Bridge-Coder: Transferring Model Capabilities from High-Resource to Low-Resource Programming Language.
Proceedings of the Findings of the Association for Computational Linguistics, 2025

VLM2-Bench: A Closer Look at How Well VLMs Implicitly Link Explicit Matching Visual Cues.
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2025

2024
Bridge-Coder: Unlocking LLMs' Potential to Overcome Language Gaps in Low-Resource Code.
CoRR, 2024

Image Textualization: An Automatic Framework for Creating Accurate and Detailed Image Descriptions.
CoRR, 2024

CORE: Mitigating Catastrophic Forgetting in Continual Learning through Cognitive Replay.
CoRR, 2024

Image Textualization: An Automatic Framework for Generating Rich and Detailed Image Descriptions.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

FIRST: Teach A Reliable Large Language Model Through Efficient Trustworthy Distillation.
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

MLLM-Protector: Ensuring MLLM's Safety without Hurting Performance.
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

2023
FuzzLLM: A Novel and Universal Fuzzing Framework for Proactively Discovering Jailbreak Vulnerabilities in Large Language Models.
CoRR, 2023


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