4 papers
From KMMLU-Redux to KMMLU-Pro: A Professional Korean Benchmark Suite for LLM Evaluation
Seokhee Hong, Sunkyoung Kim, Guijin Son +3
The development of Large Language Models (LLMs) requires robust benchmarks that encompass not only academic domains but also industrial fields to effectively evaluate their applica…
Neurosymbolic Reasoning Shortcuts under the Independence Assumption
Emile van Krieken, Pasquale Minervini, Edoardo Ponti +1
The ubiquitous independence assumption among symbolic concepts in neurosymbolic (NeSy) predictors is a convenient simplification: NeSy predictors use it to speed up probabilistic r…
Self-Training Large Language Models for Tool-Use Without Demonstrations
Ne Luo, Aryo Pradipta Gema, Xuanli He +3
Large language models (LLMs) remain prone to factual inaccuracies and computational errors, including hallucinations and mistakes in mathematical reasoning. Recent work augmented L…
Mixtures of In-Context Learners
Giwon Hong, Emile van Krieken, Edoardo Ponti +2
In-context learning (ICL) adapts LLMs by providing demonstrations without fine-tuning the model parameters; however, it does not differentiate between demonstrations and quadratica…