Large language models (LLMs) have demonstrated remarkable capabilities across a broad
spectrum of tasks. They have attracted significant attention and been deployed in numerous …

Discriminatory social biases, including gender biases, have been found in Pre-trained
Language Models (PLMs). In Natural Language Inference (NLI), recent bias evaluation …

We introduce the Dutch Model Benchmark: DUMB. The benchmark includes a diverse set of
datasets for low-, medium-and high-resource tasks. The total set of nine tasks includes four …

Instruction tuning is essential for large language models (LLMs) to become interactive. While
many instruction tuning datasets exist in English, there is a noticeable lack in other …

Reading comprehension involves the process of reading and understanding textual
information in order to answer questions related to it. It finds practical applications in various …

Large Language Models (LLMs) trained on massive corpora demonstrate impressive
capabilities in a wide range of tasks. While there are ongoing efforts to adapt these models …

Neural language models have exhibited outstanding performance in a range of downstream
tasks. However, there is limited understanding regarding the extent to which these models …

Document retrieval in many languages has been largely relying on multi-lingual models,
and leveraging the vast wealth of English training data. In Japanese, the best performing …

We report the development of Japanese SimCSE, Japanese sentence embedding models
fine-tuned with SimCSE. Since there is a lack of sentence embedding models for Japanese …

Pretrained language models underpin several AI applications, but their high computational
cost for training limits accessibility. Initiatives such as BLOOM and StarCoder aim to …