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Why is the Large Language Model (LLM) so "big" in handling Japanese?

Popularity：672 ℃/2025-02-20 16:04:32

introduction

Have you ever wondered why AI masters always scratch their heads when dealing with Japanese? In fact, this all starts with the magical little thing "token".

In large language models (LLM),tokenIt is the basic processing unit of text. Imagine that if you break a passage into a Lego building block, each token is a building block, and only by combining it can you build a wonderful language building. These tokens may be a word, a character, or even a part of a word.

So, why not just use "word" or "word"? This comes down to the "temper" of different languages.

 

Why is Tokenization so important?

  1. Understand sentence structure accurately: Just like puzzle games, the correct participle is the key clue to cracking the meaning of a sentence.

  2. Improve translation quality: In machine translation, accurate tokenization can prevent the translation results from being "talk to the same as a duck".

  3. Natural language generation: In order to make AI speak like a human, the model needs to have a "substantially" understanding of input.

Example of word participle

English patterns

Take English as an example, words like "unbelievable" can be divided into three parts: "un-", "believe", and "-able". Each section has its own "little thoughts"—the negative prefix, core verb, and adjective suffix. In this way, the model can understand that the word means "not believing".

The mystery of Chinese

Look at Chinese again: "Apple has launched a new product." It can be split into:

  • Apple

  • roll out

  • It's

  • new

  • product

  • 。

Through disassembly, the model can capture who did what and what the result is.

Twitching Japanese

However, in Japanese, it is not that simple.

 

Difficulty of Japanese Tokenization

1. A world without spaces

First, in Japanese sentencesThere are basically no spaces! Yes, you read that right, a whole string of characters doesn't even give you a chance to breathe. For example:

Private はNew yesterdayしいちどを bought いました.

The translation is: "I bought a new camera yesterday." But for AI, it's more like a mess and needs to be clarified. Possible participle results are:

  1. private(I)

  2. は(Theme mark auxiliary word)

  3. Yesterday(yesterday)

  4. New(New)

  5. カメラ(camera)

  6. を(Object mark auxiliary word)

  7. Buy いました(Born)

  8. 。

2. The "mixed style" of three texts

Japanese is simply a "mixed and matched expert" in the text world, and at the same timeChinese character、HiraganaandKatakana：

  • Chinese character: Carrying the main meanings of words, such as nouns and verb stems.

  • Hiragana: Used to represent syntactic relationships, similar to adhesives.

  • Katakana: Specializes in dealing with foreign words, onomatopoeia, or for emphasis.

3. The big challenge of ambiguity and ambiguity

In Japanese, a word may have multiple meanings, and multiple words may collide and create new sparks. Let me give you a chestnut (oh no, example):

おliquorをBeverageまないpeopleもいます。

After participle:

  1. おWine(liquor)

  2. を(Object mark auxiliary word)

  3. Drink(No drinking)

  4. people(people)

  5. も(also)

  6. います(have)

The model needs to figure out whether "drink" or "drink", and also understand the whole sentence based on the context, "there are people who don't drink." Isn't it a bit brain-burning?

 

Why is Japanese so difficult?

Comparing the "behaved" of other languages

  • English: There are spaces between words, and the change of word shape is relatively simple.

  • Chinese: Although there are no spaces, the Chinese characters themselves have a lot of information and the word segmentation algorithm is relatively mature.

  • German: Although the words are long, they are basically written in conjunction and have strong rules.

In contrast, the Japanese combination punch of "no spaces + three texts + ambiguity" makes the model unpredictable.

 

Image metaphor

Processing Japanese text is like interpreting a pictureComplex murals without borders：

  • Chinese characterIt is the fine pattern in the mural, conveying the main message.

  • HiraganaIt is the lines that connect patterns, carrying grammar and connection.

  • KatakanaIt is a prominent pattern that emphasizes special meaning or external concepts.

AI needs to identify the characteristics of each part like an artist and skillfully combine them in order to understand the meaning of the whole painting.

 

How to deal with it

To solve these problems, LLM is usually usedMorphological analysisandStatistical Model：

  1. Recognize dictionary vocabulary: Using a massive corpus, knowing which character sequences usually form a word is like building a "common phrase manual" in your mind.

  2. Probability statistics: Calculate the possibility of character combination and choose the most likely word participle method.

 

Conclusion

So, when you see AI "going crazy" next time you're dealing with Japanese, give it a little patience. After all, understanding Japanese is like solving a complex puzzle for machines. But it is these challenges that have made AI technology continue to advance and also made us sincerely admire the diversity of language.

 

Extended thinking

If you are interested in the wonders of different languages, you might as well take a look at the syllable spelling system in Korean or the sequel to Arabic. Each language has its own "password" and is waiting for us to crack it.

 

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