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https://arxiv.org/pdf/2109.01652AbstractThis paper explores a simple method for improving the zero-shot learning abilities of language models. We show that instruction tuning—finetuning language models on a collection of datasets described via instructions—substantially improves zeroshot performance on unseen tasks.  We take a 137B parameter pretrained language model and instruction tune it on o..

https://arxiv.org/pdf/2107.135867. Training Strategies for Prompting MethodsWith the methods in the above sections, it is now clear how to obtain an appropriate prompt (or prompts) and corresponding answers. Now we discuss about methods that explicitly train models in concert with prompting methods, as outlined in the “Training Strategies” section of Fig.1.7.1. Training SettingsIn many cases, pr..

https://arxiv.org/pdf/2107.13586AbstractThis paper surveys and organizes research works in a new paradigm in natural language processing, which we dub “prompt-based learning”. Unlike traditional supervised learning, which trains a model to take in an input x and predict an output y as P(y|x), prompt-based learning is based on language models that model the probability of text directly. To use th..

https://www.promptingguide.ai/https://github.com/dair-ai/Prompt-Engineering-Guidehttps://help.openai.com/en/articles/6654000-best-practices-for-prompt-engineering-with-the-openai-apiTechniquesZero-shot PromptingLarge language models (LLMs) today, such as GPT-3.5 Turbo, GPT-4, and Claude 3, are tuned to follow instructions and are trained on large amounts of data. Large-scale training makes these..

https://www.promptingguide.ai/https://github.com/dair-ai/Prompt-Engineering-Guidehttps://help.openai.com/en/articles/6654000-best-practices-for-prompt-engineering-with-the-openai-api Prompt engineering is a relatively new discipline for developing and optimizing prompts to efficiently use language models (LMs) for a wide variety of applications and research topics. Prompt engineering skills help..

https://arxiv.org/pdf/1910.106833.7. Putting It All TogetherWe now leverage the insights from our systematic study to determine how far we can push performance on popular NLP benchmarks. We are also interested in exploring the current limits of transfer learning for NLP by training larger models on large amounts of data. We start with our baseline training approach and make the following changes..

https://arxiv.org/pdf/1910.10683자, 심호흡 하고 시작하자!! ㅎㅎㅎㅎㅎ 갈 길이 어마어마하게 멀다 ㅋㅋㅋㅋㅋGoogle 연구진들이 엄청 심혈을 기울여서 experiments를 진행하고, T5를 완성하셨어 ㅋㅋㅋ이거 보다가 나 쓰러질지도 몰라. T5 자세히 봐야겠다 생각한 게 1년이 넘었다 ㅋㅋㅋ이제 마지막 기회야. 지금 지나면 이제 이렇게 시간을 들여서 볼 기회가 없을 것 같아. 후 =3 후 =3 심호흡!! 할 수 있어!! 완주할 수 있어!! ㅎㅎ3. Experiments Recent advances in transfer learning for NLP have come from a wide variety of developments, such as new pre-training..

https://arxiv.org/pdf/1910.10683AbstractTransfer learning, where a model is first pre-trained on a data-rich task before being finetuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer le..