Published in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV 2025), 2025
This paper was accepted to ICCV 2025 conference.
Recommended citation: J Geng, et al. (2025). "Paper Title." Proceedings of the IEEE/CVF International Conference on Computer Vision.
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Published in Findings of the Association for Computational Linguistics: EMNLP 2025, 2025
This paper introduces CaMMT, a benchmark for evaluating culturally aware multimodal machine translation systems.
Recommended citation: E Villa-Cueva, S Bolatzhanova, D Turmakhan, J Geng, et al. (2025). "CaMMT: Benchmarking Culturally Aware Multimodal Machine Translation." Findings of the Association for Computational Linguistics: EMNLP 2025.
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Published in Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), 2025
This paper presents Marco-Bench-MIF, a benchmark for evaluating multilingual instruction-following capabilities of large language models.
Recommended citation: B Zeng, C Lyu, S Liu, M Zeng, M Wu, X Ni, T Shi, Y Zhao, Y Liu, C Zhu, et al. (2025). "Marco-Bench-MIF: On multilingual instruction-following capability of large language models." Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics.
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Published in ACL 2025 Main, 2025
This paper introduces HD-NDEs, a novel approach using neural differential equations for detecting hallucinations in large language models.
Recommended citation: Q Li, J Geng, Z Chen, D Zhu, Y Wang, C Ma, C Lyu, F Karray. (2025). "HD-NDEs: Neural Differential Equations for Hallucination Detection in LLMs." ACL 2025 Main.
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Published in ACL 2025 Main, 2025
This paper explores methods for understanding and defending against jailbreak attacks in large language models.
Recommended citation: L Gao, J Geng, X Zhang, P Nakov, X Chen. (2025). "Shaping the Safety Boundaries: Understanding and Defending Against Jailbreaks in Large Language Models." ACL 2025 Main.
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Published in ACL 2025 Findings, 2025
This paper introduces VSCBench, a comprehensive benchmark for evaluating safety calibration in vision-language models.
Recommended citation: J Geng, Q Li, Z Chen, Y Wang, D Zhu, Z Xie, C Lyu, X Chen, P Nakov, et al. (2025). "VSCBench: Bridging the Gap in Vision-Language Model Safety Calibration." ACL 2025 Findings.
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Published in ACL 2025 Main, 2025
This paper presents a universal jailbreaking method for multimodal large language models using non-textual modalities.
Recommended citation: J Geng, TT Tran, P Nakov, I Gurevych. (2025). "Con Instruction: Universal Jailbreaking of Multimodal Large Language Models via Non-Textual Modalities." ACL 2025 Main.
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Published in Proceedings of the 39th AAAI Conference on Artificial Intelligence (AAAI 2025), 2024
This paper was accepted to AAAI 2025 conference.
Recommended citation: J Geng, et al. (2025). "Paper Title." Proceedings of the 39th AAAI Conference on Artificial Intelligence.
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Published in arXiv preprint, 2025
This paper presents CoQuIR, a comprehensive benchmark for evaluating code quality-aware information retrieval systems.
Recommended citation: J Geng, F Cai, S Cui, Q Li, L Chen, C Lyu, H Li, D Zhu, W Pretschner, et al. (2025). "CoQuIR: A Comprehensive Benchmark for Code Quality-Aware Information Retrieval." arXiv preprint arXiv:2506.11066.
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