1 citations · 1 across the 7 of their papers we have counts for
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Every Wrong Answer Counts: Option-Level Psychometrics for LLM Multiple-Choice Benchmarks
Xiao Fei, Yang Zhang, Sarah Almeida Carneiro +1
Most multiple-choice question (MCQ) benchmarks evaluate Large Language Models (LLMs) only by whether they select the correct answers. This binary scoring treats all incorrect respo…
CARTE: A Benchmark for Mapping Language Model Knowledge Across France
Sarah Almeida Carneiro, Christos Xypolopoulos, Xiao Fei +2
We introduce CARTE 1 (Culturally Anchored Regional-Territorial Evaluation), a multiplechoice benchmark for evaluating the ability of large language models (LLMs) to perform fine-gr…
TrustLDM: Benchmarking Trustworthiness in Language Diffusion Models
Yichuan Mo, Yukun Jiang, Yanbo Shi +4
The rapid development of Language Diffusion Models (LDMs) challenges the dominant position of auto-regressive competitors in language processing. However, their flexible, any-order…
GreekMMLU: A Native-Sourced Multitask Benchmark for Evaluating Language Models in Greek
Yang Zhang, Mersin Konomi, Christos Xypolopoulos +6
Large Language Models (LLMs) are commonly trained on multilingual corpora that include Greek, yet reliable evaluation benchmarks for Greek-particularly those based on authentic, na…
Beyond Random Sampling: Efficient Language Model Pretraining via Curriculum Learning
Yang Zhang, Amr Mohamed, Hadi Abdine +2
Curriculum learning-organizing training data from easy to hard-has improved efficiency across machine learning domains, yet remains underexplored for language model pretraining. We…
Fast-Decoding Diffusion Language Models via Progress-Aware Confidence Schedules
Amr Mohamed, Yang Zhang, Michalis Vazirgiannis +1
Diffusion large language models (dLLMs) offer a promising alternative to autoregressive models, but their practical utility is severely hampered by slow, iterative sampling. We pre…