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Chengyu Jiao

2 accepted papers

2026

Embracing Positional Bias in Multiple-Choice Question Answering via Permutation Equivariant Neural Networks

AAAI 2026technical

Several studies have demonstrated that large language models (LLMs) exhibit positional bias when answering multiple-choice questions (MCQs). Previous methods have identified such bias to be detrimental, leading to the development of techniques to mitigate it. However, we observe that certain permuta

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2025

SPE Attention: Making Attention Equivariant to Semantic-Preserving Permutation for Code Processing

EMNLP 2025

Codes serve as the fundamental language for human to communicate with machines, and various Transformer-based models are trained to process codes in recent advancements. A unique symmetry of code is its semantic-preserving permutation, which allows certain lines to be rearranged without altering the

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