ToC_lab

Theory of Computation Lab, YONSEI University

[ToC_lab @ToC_lab]

Thank you for reading. For more details, please check out our paper: https://t.co/yzbBa0HobA (5/5)

[ToC_lab @ToC_lab]

📣 Excited to share our new paper "RegexPSPACE: A Benchmark for Evaluating LLM Reasoning on PSPACE-complete Regex Problems"! (1/5)
Paper: https://t.co/A3asGnRYzU
Code: https://t.co/jTbNyqw7BN
#AIResearch #Regex #PSPACE-complete #Benchmark #ReasoningTasks

[ToC_lab @ToC_lab]

This work highlights a critical gap in the spatial reasoning capabilities of current models, identifying key failure modes like repetition. We provide a rigorous new framework for future research. (4/5)

[ToC_lab @ToC_lab]

🚧 What’s next for #TCProF? Future directions include extending to dynamic complexity calculation, space-complexity prediction, generalizing across more programming languages, and adapting to zero-shot scenarios. Excited for more advances ahead! 🌐📌

[ToC_lab @ToC_lab]

🚀Just released! We will introduce "TCProF: Time-Complexity Prediction SSL Framework" at #NAACL2025. Ideal for programming competitions and code education! 🎉💻
Paper: https://t.co/fiOhQ44e8q
Code: https://t.co/DWJIqJwhhc
#AIResearch #CodeTimeComplexity #SemiSupervisedLearning

[ToC_lab @ToC_lab]

📈 #TCProF achieves groundbreaking performance, improving accuracy by over 60% compared to standard self-training and outperforming state-of-the-art methods like JointMatch by up to 131%! Reliable even with minimal labeled data. 💪

[ToC_lab @ToC_lab]

🧩 At the heart of #TCProF is our symbolic module—leveraging regex matching and AST parsing to systematically identify loops, recursions, and complexity-critical patterns, enabling precise pseudo-labeling without neural biases! 🔍🔗

[ToC_lab @ToC_lab]

📌Introducing #TCProF, our innovative SSL framework tailored for code complexity prediction! By effectively combining data augmentation, symbolic analysis, and co-training, TCProF robustly addresses data scarcity, greatly boosting prediction accuracy. 📊✨

[ToC_lab @ToC_lab]

🔍 Curious about improving implicit hate speech detection just through data selection?
Our paper introduces CONELA, showing how selecting training data based on human agreement patterns & model dynamics can significantly boost performance - without changing model architecture!

[ToC_lab @ToC_lab]

📣 Excited to share our new paper "Analyzing Offensive Language Dataset Insights from Training Dynamics and Human Agreement Level" at #COLING2025! 🎉

Paper: https://t.co/y0kog2RdSa
CONELA Code: https://t.co/mEWHMGBKeq
#NLP2025 #dk_search_ai #finetuning #hatespeechdetection

[ToC_lab @ToC_lab]

@coling2025 @dk_search_ai

[ToC_lab @ToC_lab]

@coling2025

[ToC_lab @ToC_lab]

Paper: https://t.co/HfTKYpl3P5
Github: https://t.co/OBwgLH5CGF

[ToC_lab @ToC_lab]

Come on and take a look at our dataset “CodeComplex”:
A dataset for the worst-case time complexity prediction on code competitions.
Summary:

[ToC_lab @ToC_lab]

That’s the dataset in a nutshell!
This work created by me (Seung-Yeop Baik) with the help of Joonghyuk Hahn, Jungin Kim, Mingi Jeon, Aditi, Yo-Sub Han, and Sang-Ki Ko.If you want to access the dataset or get into further details, follow the links below to get the full picture!