Macaw by AI2
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人工智能GPT-3替代大语模型(LLMS)

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多角度c(q)uestion回答

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金刚鹦鹉(多角度C(q)uestion响应)是一种能够使用一般问题答案的现成模型,显示了经过训练的域之外的稳健性。它已经接受了“多角度”方式训练,这意味着它可以处理一组灵活的输入和输出“插槽”(例如问题,答案,解释)。

金刚鹦鹉是在[t5](https://github.com/google-research/text-text-tex-text-transfer-transformer)顶部建造的,有不同的尺寸,以及各种排行榜上的以答案为中心的版本:Macaw-andwer-11b(见下文)。

即使金刚鹦鹉的数量级比GPT-3小(仅使用110亿参数与GPT-3的1750亿参数),但它的表现优于流行模型,这使其在强大语言模型的商品化中是非常有希望的演变。

金刚鹦鹉可以成功解决广泛的问题类型,包括通用知识,元推理,假设和故事理解。 AI2的Aristo团队开发了金刚鹦鹉,这是他们为构建可以阅读,推理和解释其答案的AI系统努力的一部分。

原文:

Macaw (Multi-angle c(q)uestion answering) is a ready-to-use model capable of general question answering, showing robustness outside the domains it was trained on. It has been trained in "multi-angle" fashion, which means it can handle a flexible set of input and output "slots" (like question, answer, explanation) .

Macaw was built on top of [T5](https://github.com/google-research/text-to-text-transfer-transformer) and comes in different sizes: macaw-11b, macaw-3b, and macaw-large, as well as an answer-focused version featured on various leaderboards: macaw-answer-11b (see below).

Even though Macaw is an order of magnitude smaller than GPT-3 (using just 11 billion parameters to GPT-3’s 175 billion parameters), it outperforms the popular model, making it a very promising evolution in the commoditization of powerful language models.

Macaw can successfully tackle a broad range of question types including general knowledge, meta reasoning, hypothetical, and story understanding. AI2’s Aristo team developed Macaw as part of their efforts toward building AI systems that can read, reason, and explain their answers.

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