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SeungYeop Baik

2 accepted papers

2025

CodeComplex: Dataset for Worst-Case Time Complexity Prediction

EMNLP 2025

Reasoning ability of large language models (LLMs) is a crucial ability,especially in complex decision-making tasks. One significant task to show LLMs’reasoning capability is code time complexity prediction, which involves variousintricate factors such as the input range of variables and conditional

2025

TrapDoc: Deceiving LLM Users by Injecting Imperceptible Phantom Tokens into Documents

EMNLP 2025

The reasoning, writing, text-editing, and retrieval capabilities of proprietary large language models (LLMs) have advanced rapidly, providing users with an ever-expanding set of functionalities. However, this growing utility has also led to a serious societal concern: the over-reliance on LLMs. In p