Inverse IFEval: Can LLMs Unlearn Stubborn Training Conventions to Follow
  Real Instructions?
Authors: name: Qinyan Zhang, name: Xinping Lei, name: Ruijie Miao, name: Yu Fu, name: Haojie Fan, name: Le Chang, name: Jiafan Hou, name: Dingling Zhang, name: Zhongfei Hou, name: Ziqiang Yang, name: Changxin Pu, name: Fei Hu, name: Jingkai Liu, name: Mengyun Liu, name: Yang Liu, name: Xiang Gao, name: Jiaheng Liu, name: Tong Yang, name: Zaiyuan Wang, name: Ge Zhang, name: Wenhao Huang
Organization: ByteDance Seed

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 benchmark that
measures models Counter-intuitive Abilitytheir capacity to override
training-induced biases and comply with adversarial instructions. Inverse
IFEval introduces eight types of such challenges, including Question
Correction, Intentional Textual Flaws, Code without Comments, and
Counterfactual Answering. Using a human-in-the-loop pipeline, we construct a
dataset of 1012 high-quality Chinese and English questions across 23 domains,
evaluated under an optimized LLM-as-a-Judge framework. Experiments on existing
leading LLMs demonstrate the necessity of our proposed Inverse IFEval
benchmark. Our findings emphasize that future alignment efforts should not only
pursue fluency and factual correctness but also account for adaptability under
unconventional contexts. We hope that Inverse IFEval serves as both a
diagnostic tool and a foundation for developing methods that mitigate cognitive
inertia, reduce overfitting to narrow patterns, and ultimately enhance the
instruction-following reliability of LLMs in diverse and unpredictable
real-world scenarios.