52 citations · 56 across the 4 of their papers we have counts for
6 papers
Causes in neuron diagrams, and testing causal reasoning in Large Language Models. A glimpse of the future of philosophy?
Louis Vervoort, Vitaly Nikolaev
We propose a test for abstract causal reasoning in AI, based on scholarship in the philosophy of causation, in particular on the neuron diagrams popularized by D. Lewis. We illustr…
TaTa: A Multilingual Table-to-Text Dataset for African Languages
Sebastian Gehrmann, Sebastian Ruder, Vitaly Nikolaev +4
Existing data-to-text generation datasets are mostly limited to English. To address this lack of data, we create Table-to-Text in African languages (TaTa), the first large multilin…
Planning with Learned Entity Prompts for Abstractive Summarization
Shashi Narayan, Yao Zhao, Joshua Maynez +3
We introduce a simple but flexible mechanism to learn an intermediate plan to ground the generation of abstractive summaries. Specifically, we prepend (or prompt) target summaries…
The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
Sebastian Gehrmann, Tosin Adewumi, Karmanya Aggarwal +53
We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics. Measuring progress in NLG relies on a constantly evolving ecosystem of auto…
Convolution Neural Networks for Semantic Segmentation: Application to Small Datasets of Biomedical Images
Vitaly Nikolaev
This thesis studies how the segmentation results, produced by convolutional neural networks (CNN), is different from each other when applied to small biomedical datasets. We use di…
TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages
Jonathan H. Clark, Eunsol Choi, Michael Collins +4
Confidently making progress on multilingual modeling requires challenging, trustworthy evaluations. We present TyDi QA---a question answering dataset covering 11 typologically dive…