3 papers
cs.CY2025
TRAPDOC: Deceiving LLM Users by Injecting Imperceptible Phantom Tokens into Documents
Hyundong Jin, Sicheol Sung, Shinwoo Park +2
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 fu…
cs.SE2024
CodeComplex: Dataset for Worst-Case Time Complexity Prediction
Seung-Yeop Baik, Joonghyuk Hahn, Jungin Kim +4
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…
cs.CL2024
A Framework for Quantum Finite-State Languages with Density Mapping
SeungYeop Baik, Sicheol Sung, Yo-Sub Han
A quantum finite-state automaton (QFA) is a theoretical model designed to simulate the evolution of a quantum system with finite memory in response to sequential input strings. We…