シャドーイング練習: Introduction to Generative AI and LLMs [Pt 1] | Generative AI for Beginners - 動画で英語スピーキングを学ぶ

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Hi everyone and welcome to the first lesson of the Generative AI for Beginners course.

This course is based on an open source curriculum with the same name available on GitHub

that you can find at the link on the screen.

I'm Carlotta Castelluccio, I'm a cloud advocate at Microsoft focused on artificial intelligence technologies

and in this video I'm going to introduce you to generative AI and large language models.

Large language models represent the pinnacle of AI technology, pushing the boundaries of what was once thought impossible.

They've conquered numerous challenges that older language models struggled with, achieving human-leavened performance in various tasks.

They have similar capabilities and applications, but for the sake of this course,

we'll explore how large language models are revolutionizing education through a fictional startup that we'll be referring to as Our Startup.

Our startup works in the education domain with the ambitious mission of improving accessibility in learning on a global scale,

ensuring equitable access to education, and providing personalized learning experiences to every learner, according to their needs.

In this course, we'll delve into how our startup harnesses the power of generative AI to unlock new possibilities in education.

We'll also examine how they address the inevitable challenges tied to the social impact of this technology and its technological limitations.

But let's start by defining some basic concepts we'll be using throughout the course.

Despite the relatively recent hype surrounding generative AI,

we can say that in the last couple of years we have really heard of generative AI everywhere and every time.

But this technology has been decades in the making, with its origins tracing back to the 1950s, 1960s.

The earliest AI prototypes consisted of typewritten chatbots relying on knowledge bases maintained experts.

These chatbots generated responses based on keywords found in user input,

but it soon became clear that this approach had scalability limitations.

A significant turning point arrived in the 1990s when a statistical approach was applied to text analysis.

And this gave birth to machine learning algorithms which could learn patterns from data without explicit programming.

And these algorithms allowed machines to stimulate human language understanding, having the way for the eye we know today.

In more recent times, advancements in hardware technology allowed for development of advanced machine learning algorithms, particularly neural networks.

These innovations significantly improved natural language processing, enabling machines to understand the context of words in sentences.

This breakthrough technology powered the birth of virtual assistants in the early 21st century.

This virtual assistants excelled at interpreting human language, identifying needs, and taking actions to fulfill them,

such as answering queries with predefined scripts or connecting to third-party services.

And so we arrived at Generative AI, a subset of deep learning.

After decades of AI research,

a new model architecture known as the Transformer emerged transformers could handle longer text sequences as input

and work based on the attention mechanism, enabling them to focus on the most relevant information,

regardless of its order in the input text.

Today, mass-generative AI models, often referred to as large language models,

are built upon the transformer architecture, and that's what the T in GPT actually means.

These models trained on vast amounts of data from sources like books, articles, and websites possess a unique adaptability.

They can tackle a wide range of tasks and generate chromatically correct text with a hint of creativity.

But let's dive deeper into the mechanism of large language models

and shed light on the inner workings of models like the OpenAI GPT's.

One of the key concepts to grasp is tokenization.

Large language models receive text as input and produce text as output, if you want to really simplify the mechanism.

However, these models work much more efficiently with numbers rather than with raw text sequences.

And that's where the tokenizer comes into play.

Text prompts are chunked into tokens, helping the model in predicting the next token for completion.

Models also have a maximum length of token window,

and model pricing is also tightly computed by the number of tokens used in output and inputs.

So tokenization is really an important concept in large language models and generative AI domain.

Now, a token is essentially a chunk of text which can vary in length and typically consists of a sequence of characters.

And the tokenizer's primary job is to really break down the input text into an array of those tokens,

which are then further mapped to token indices.

These token indices are essentially integer and coding of the original text chunks, making it easier for the model to process and understand.

Now let's move to predicting the output tokens.

Given an input sequence of and tokens with the maximum and varying from one model to another according to the maximum

content window length of for one model, the model is designed to predict a single token as its output.

But here's where it gets interesting.

The predicted token is then incorporated into the input of the next iteration, creating an expanding window pattern.

And this pattern allows the model to provide more coherent and contextually relevant responses,

often extending to one or multiple sentences.

Now let's delve into the selection process.

The model chooses the output token based on its probability of occurring after the current text sequence.

This probability distribution is calculated using the model's training data.

However, here's the twist.

The model doesn't always choose the token with the highest probability from the distribution.

To simulate the process of creative thinking, a degree of randomness is introduced into the selection process.

This means that the model doesn't produce the exact same output for the same input every time.

That's the element that allows generative AI to generate text that feels, you know, creative and engaging.

Now, we see that the main capability of a large language model is generating text from scratch, starting from a textual input written in natural language.

But what kind of textual input and output?

First of all, let me say that the input of a large language model is known as prompt, while the output is known as completion,

a term that refers to the model mechanism of generating the next token to complete the current input.

Let's do some examples of prompts and completion by using the OpenAI ChatGPT Playground, always in our educational scenario.

Now, a prompt may include an instruction specifying the type of output we expect from the model.

In the example we are seeing, we are asking to write an assignment for high school students,

including four open-ended questions about Louis XIV and his court.

You can see that the output is exactly what I'm asking for.

The model was able to generate an assignment with the questions.

Now, another prompt might be a question, asking in the form of a conversation with an agent.

In this example, we are asking about in a question.

So we asked who is Louis XIV and why he is an important historical character?

And we've got an answer.

Another type of prompt might be a chunk of text to complete.

So an incipit of a text to complete.

And you can see now that we used an incipit of a text to complete to complete as prompt,

and we've got a whole paragraph to complete the current input.

So this is basically an implicit ask for writing assistance.

Now, the examples I just did are quite simple, and don't want to be an exhaustive demonstration of large language models capabilities.

They just want to show you the potential of using Generative AI,

in particular, but not limited in a context such as the educational context we have used today as an example.

That's all for now.

In the following lesson, we are going to explore different types of generative AI models.

And we're going to cover also how to test, to iterate, and to improve the performance,

and compare also different models to find the most suitable one for a specific use case.

Thank you.

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このレッスンの語彙とスピーキングのポイント

このC1レベルのスピーキングレッスンは、動画「Introduction to Generative AI and LLMs」を教材にしています。繰り返し出てくる語は次のとおりです：model, text, token, language, input。この動画には、シャドーイング用の文が93文、単語が1349語あります。音声の長さは10:25です。話す速さは1分あたり約129語で安定しており、シャドーイングしやすいペースです。英語の頻出3,000語に含まれる単語は75%だけなので、語彙は難しめです。

この動画の重要語彙

動画の中で特に難しい単語15語を、発音と意味つきで紹介します。

単語	発音	意味
token 名詞	/ˈtoʊkən/	トークン
chunk 名詞	/t͡ʃʌŋk/	塊
startup 名詞	/ˈstɑɹtˌʌp/	スタートアップ, ベンチャー
predict 動詞	/pɹɪˈdɪkt/	予言する
transformer 名詞	/tɹænsˈfoɹməɹ/	変圧器, 変流器
probability 名詞	/ˌpɹɑ.bəˈbɪl.ə.ti/	確率
algorithm 名詞	/ˈælɡəɹɪðm̩/	アルゴリズム, 演算手順
capability 名詞	/ˌkeɪ.pəˈbɪl.ɪ.ti/	能力
chatbot 名詞	/ˈtʃætbɑt/	チャットボット
delve 動詞	/dɛlv/	ほる
consist 名詞	/kənˈsɪst/	編成
compute 動詞	/kəmˈpjuːt/	計算する
implicit 形容詞	/ɪmˈplɪsɪt/	暗黙の
integer 名詞	/ˈɪn.tɪ.d͡ʒə(ɹ)/	整数
keyword 名詞	/ˈkiː.wɜːd/	キーワード

この動画の文法

話し手がよく使っている文型を、動画の実際の表現とともに紹介します。

文型	動画での表現
受動態 be + 過去分詞 — 誰がするかより、何が起きるかに焦点を当てる	is based · was applied · are built
現在完了形 have/has + 過去分詞 — 過去の出来事が今も関係している	They've conquered · have really heard · has been

注意したい発音

話し手はwe'll, I'm, doesn'tなど、短縮形や弱形を16回使っています。聞こえたとおりの短い形で発音しましょう。

「th」の音: algorithm /ˈælɡəɹɪðm̩/, breakthrough /ˈbɹeɪk.θɹuː/
「sh」と「zh」の音: limitation /lɪmɪˈteɪʃən/, iteration /ˌɪt.əˈɹeɪ.ʃən/, revolutionize /ˌɹɛv.əˈl(j)u.ʃəˌnaɪz/, fictional /ˈfɪkʃənəl/, ambitious /æmˈbɪʃ.əs/
長い単語（アクセントの位置に注意）: generative /ˈd͡ʒɛnəɹətɪv/, probability /ˌpɹɑ.bəˈbɪl.ə.ti/, capability /ˌkeɪ.pəˈbɪl.ɪ.ti/, limitation /lɪmɪˈteɪʃən/, adaptability /ədæptəˈbɪlɪti/

日本語話者が苦手な音：

/r/ と /l/ の区別 — /l/ は舌先を歯茎につけ、/r/ はどこにもつけない: probability /ˌpɹɑ.bəˈbɪl.ə.ti/, algorithm /ˈælɡəɹɪðm̩/, chromatically /kroʊˈmæt̬.ɪk.li/, learner /ˈlɜɹnəɹ/, revolutionize /ˌɹɛv.əˈl(j)u.ʃəˌnaɪz/
/v/ — /b/ にならないように、上の歯を下唇に当てる: generative /ˈd͡ʒɛnəɹətɪv/, delve /dɛlv/, exhaustive /ɪɡˈzɔstɪv/, leaven /ˈlɛv.ən/, revolutionize /ˌɹɛv.əˈl(j)u.ʃəˌnaɪz/
/f/ — 「フ」ではなく、上の歯と下唇で出す: transformer /tɹænsˈfoɹməɹ/, simplify /ˈsɪmplɪfaɪ/, specify /ˈspɛs.əˌfaɪ/, fulfill /fʊlˈfɪl/, fictional /ˈfɪkʃənəl/

この動画での練習方法

まず声を出さずに動画を最後まで聞き、知らない単語をメモします。
通常の速度で一文ずつシャドーイングし、話し手のリズムに合うまで繰り返します。
自分の声を録音して元の音声と比べます。token, chunk, startupなどの単語に特に注意しましょう。

シャドーイングとは？英語上達に効果的な理由

シャドーイング（Shadowing）は、もともとプロの通訳者養成プログラムで開発された言語学習法で、多言語習得者として知られるDr. Alexander Arguelles によって広く普及されました。方法はシンプルですが非常に効果的：ネイティブスピーカーの英語を聞きながら、1〜2秒の遅延で声に出してすぐに繰り返す——まるで「影（shadow）」のように話者を追いかけます。文法ドリルや受動的なリスニングと異なり、シャドーイングは脳と口の筋肉が同時にリアルタイムで英語を処理・再現することを強制します。研究により、発音精度、抑揚、リズム、連音、リスニング力、そして会話の流暢さが大幅に向上することが確認されています。IELTSスピーキング対策や自然な英語コミュニケーションを目指す方に特におすすめです。

シャドーイングのやり方: ステップ別の完全ガイドを読む →

シャドーイング練習: Introduction to Generative AI and LLMs [Pt 1] | Generative AI for Beginners - 動画で英語スピーキングを学ぶ

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