Update README.md

README.md

@@ -7,6 +7,10 @@ widget:
   example_title: real
 - text: "In Finnish folklore, all places and things, animate or inanimate, have a spirit or \"etiäinen\" that lives there. Etiäinen can manifest in many forms, but is usually described as a kind, elderly woman with white hair. She is the guardian of natural places and often helps people in need. Etiäinen has been a part of Finnish culture for centuries and is still widely believed in today. Folklorists study etiäinen to understand Finnish traditions and how they have changed over time."
   example_title: generated
+- text: "This paper presents a novel framework for sparsity-certifying graph decompositions, which are important tools in various areas of computer science, including algorithm design, complexity theory, and optimization. Our approach is based on the concept of \"cut sparsifiers,\" which are sparse graphs that preserve the cut structure of the original graph up to a certain error bound. We show that cut sparsifiers can be efficiently constructed using a combination of spectral techniques and random sampling, and we use them to develop new algorithms for decomposing graphs into sparse subgraphs."
+  example_title: from-ChatGPT
+- text: "Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples. By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do. Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic. At the same time, we also identify some datasets where GPT-3's few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora. Finally, we find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans. We discuss broader societal impacts of this finding and of GPT-3 in general."
+  example_title: GPT-3 paper
 datasets:
 - NicolaiSivesind/human-vs-machine
 language: