23 citations · 39 across the 20 of their papers we have counts for
7 papers · 1 filter
Estimating problem difficulty without ground truth using Large Language Model comparisons
Marthe Ballon, Andres Algaba, Brecht Verbeken +2
Recent advances in the finetuning of large language models (LLMs) have significantly improved their performance on established benchmarks, emphasizing the need for increasingly dif…
Lexical Hints of Accuracy in LLM Reasoning Chains
Arne Vanhoyweghen, Brecht Verbeken, Brecht F. Verbeken +2
Fine-tuning Large Language Models (LLMs) with reinforcement learning to produce an explicit Chain-of-Thought (CoT) before answering produces models that consistently raise overall…
Decision-centric fairness: Evaluation and optimization for resource allocation problems
Simon De Vos, Jente Van Belle, Andres Algaba +2
Data-driven decision support tools play an increasingly central role in decision-making across various domains. In this work, we focus on binary classification models for predictin…
How Deep Do Large Language Models Internalize Scientific Literature and Citation Practices?
Andres Algaba, Vincent Holst, Floriano Tori +5
The spread of scientific knowledge depends on how researchers discover and cite previous work. The adoption of large language models (LLMs) in the scientific research process intro…
Flexible Counterfactual Explanations with Generative Models
Stig Hellemans, Andres Algaba, Sam Verboven +1
Counterfactual explanations provide actionable insights to achieve desired outcomes by suggesting minimal changes to input features. However, existing methods rely on fixed sets of…
The relationship between reasoning and performance in large language models--o3 (mini) thinks harder, not longer
Marthe Ballon, Andres Algaba, Vincent Ginis
Large language models have demonstrated remarkable progress in mathematical reasoning, leveraging chain-of-thought and reinforcement learning. However, many open questions remain r…
Humanity's Last Exam
Long Phan, Alice Gatti, Ziwen Han +1168
Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achi…