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Dingling Zhang

2 accepted papers

2026

Inverse IFEval: Can LLMs Unlearn Stubborn Training Conventions to Follow Real Instructions?

ICLR 2026poster

Large Language Models (LLMs) achieve strong performance on diverse tasks but often exhibit cognitive inertia, struggling to follow instructions that conflict with the standardized patterns learned during supervised fine-tuning (SFT). To evaluate this limitation, we propose Inverse IFEval, a benchmar…

Cited by 0SourceScholar
2026

OmniVideoBench: Towards Audio-Visual Understanding Evaluation for Omni MLLMs

ICLR 2026poster

Recent advances in multimodal large language models (MLLMs) have demonstrated substantial potential in video understanding. However, existing benchmarks fail to comprehensively evaluate synergistic reasoning capabilities across audio and visual modalities, often neglecting either one of the modaliti…

Cited by 0SourcecodeScholar