47 citations · 55 across the 5 of their papers we have counts for
6 papers
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
BigScience Workshop, :, Teven Le Scao +391
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to wi…
TAPE: Assessing Few-shot Russian Language Understanding
Ekaterina Taktasheva, Tatiana Shavrina, Alena Fenogenova +11
Recent advances in zero-shot and few-shot learning have shown promise for a scope of research and practical purposes. However, this fast-growing area lacks standardized evaluation…
WikiOmnia: generative QA corpus on the whole Russian Wikipedia
Dina Pisarevskaya, Tatiana Shavrina
The General QA field has been developing the methodology referencing the Stanford Question answering dataset (SQuAD) as the significant benchmark. However, compiling factual questi…
mGPT: Few-Shot Learners Go Multilingual
Oleh Shliazhko, Alena Fenogenova, Maria Tikhonova +3
Recent studies report that autoregressive language models can successfully solve many NLP tasks via zero- and few-shot learning paradigms, which opens up new possibilities for usin…
Russian SuperGLUE 1.1: Revising the Lessons not Learned by Russian NLP models
Alena Fenogenova, Maria Tikhonova, Vladislav Mikhailov +6
In the last year, new neural architectures and multilingual pre-trained models have been released for Russian, which led to performance evaluation problems across a range of langua…
How not to Lie with a Benchmark: Rearranging NLP Leaderboards
Shavrina Tatiana, Malykh Valentin
Comparison with a human is an essential requirement for a benchmark for it to be a reliable measurement of model capabilities. Nevertheless, the methods for model comparison could…