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Register a new userLanguage Models are Few-shot Multilingual Learners
نماذج اللغة هي عدد قليل من المتعلمين متعدد اللغات
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أظهرت نماذج اللغة للأغراض العامة قدرات مثيرة للإعجاب، وأداء على قدم المساواة مع النهج الحديثة على مجموعة من مهام ومعايير معالجة اللغة الطبيعية المصب (NLP) عند استنتاج التعليمات من الأمثلة القليلة للغاية.هنا، نقيم المهارات متعددة اللغات في نماذج GPT و T5 في إجراء تصنيف متعدد الفئات على اللغات غير الإنجليزية دون أي تحديثات معلمة.نظهر أنه بالنظر إلى عدد قليل من الأمثلة الإنجليزية كسياق، يمكن أن تتنبأ نماذج اللغة المدربة مسبقا بعينات اختبار اللغة الإنجليزية فقط ولكن أيضا غير الإنجليزية منها.أخيرا، نجد نتائج التنبؤ القليل من الطوابق في السياق لنماذج اللغة أفضل بكثير من التنبؤ العشوائي، وهي تنافسية مقارنة بالموديلات المتبادلة الحالية من أحدث ونماذج الترجمة الحالية.
General-purpose language models have demonstrated impressive capabilities, performing on par with state-of-the-art approaches on a range of downstream natural language processing (NLP) tasks and benchmarks when inferring instructions from very few examples. Here, we evaluate the multilingual skills of the GPT and T5 models in conducting multi-class classification on non-English languages without any parameter updates. We show that, given a few English examples as context, pre-trained language models can predict not only English test samples but also non-English ones. Finally, we find the in-context few-shot cross-lingual prediction results of language models are significantly better than random prediction, and they are competitive compared to the existing state-of-the-art cross-lingual models and translation models.
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When scaled to hundreds of billions of parameters, pretrained language models such as GPT-3 (Brown et al., 2020) achieve remarkable few-shot performance. However, enormous amounts of compute are required for training and applying such big models, res
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training separate parsers. We improve upon this paradigm by proposing a novel chart-based method and an effective top-K ensemble technique. Moreover, we demonstrate that we can broaden the scope of application of the approach into multilingual settings. Specifically, we show that by applying our method on multilingual PLMs, it becomes possible to induce non-trivial parses for sentences from nine languages in an integrated and language-agnostic manner, attaining performance superior or comparable to that of unsupervised PCFGs. We also verify that our approach is robust to cross-lingual transfer. Finally, we provide analyses on the inner workings of our method. For instance, we discover universal attention heads which are consistently sensitive to syntactic information irrespective of the input language.
Natural Language Processing (NLP) is increasingly relying on general end-to-end systems that need to handle many different linguistic phenomena and nuances. For example, a Natural Language Inference (NLI) system has to recognize sentiment, handle num
bers, perform coreference, etc. Our solutions to complex problems are still far from perfect, so it is important to create systems that can learn to correct mistakes quickly, incrementally, and with little training data. In this work, we propose a continual few-shot learning (CFL) task, in which a system is challenged with a difficult phenomenon and asked to learn to correct mistakes with only a few (10 to 15) training examples. To this end, we first create benchmarks based on previously annotated data: two NLI (ANLI and SNLI) and one sentiment analysis (IMDB) datasets. Next, we present various baselines from diverse paradigms (e.g., memory-aware synapses and Prototypical networks) and compare them on few-shot learning and continual few-shot learning setups. Our contributions are in creating a benchmark suite and evaluation protocol for continual few-shot learning on the text classification tasks, and making several interesting observations on the behavior of similarity-based methods. We hope that our work serves as a useful starting point for future work on this important topic.
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