1 citations · 1 across the 3 of their papers we have counts for
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How far can bias go? Tracing bias from pretraining data to alignment
Marion Thaler, Abdullatif Köksal, Alina Leidinger +2
As LLMs are increasingly integrated into user-facing applications, addressing biases that perpetuate societal inequalities is crucial. While much work has gone into measuring or mi…
MURI: High-Quality Instruction Tuning Datasets for Low-Resource Languages via Reverse Instructions
Abdullatif Köksal, Marion Thaler, Ayyoob Imani +3
Instruction tuning enhances large language models (LLMs) by aligning them with human preferences across diverse tasks. Traditional approaches to create instruction tuning datasets…
SYNTHEVAL: Hybrid Behavioral Testing of NLP Models with Synthetic CheckLists
Raoyuan Zhao, Abdullatif Köksal, Yihong Liu +3
Traditional benchmarking in NLP typically involves using static held-out test sets. However, this approach often results in an overestimation of performance and lacks the ability t…
TurkishMMLU: Measuring Massive Multitask Language Understanding in Turkish
Arda Yüksel, Abdullatif Köksal, Lütfi Kerem Şenel +2
Multiple choice question answering tasks evaluate the reasoning, comprehension, and mathematical abilities of Large Language Models (LLMs). While existing benchmarks employ automat…