How does an llm work with RTL Hebrew grammar (verbatim)

✨AI Mode

An Large Language Model (LLM) processes Hebrew by treating it as a sequence of tokens that happens to be displayed from right to left, while its internal logic focuses on the statistical relationships between those tokens. 

1. Tokenization and Sequence

• Linear Processing: Despite being a Right-to-Left (RTL) language, Hebrew is stored and processed by LLMs as a sequential stream of Unicode characters. The model "reads" the tokens in the order they are written (Right to Left), assigning each a numerical ID.
• Morphological Challenges: Hebrew is a morphologically rich language where words are built from three-consonant roots (trilateral roots) modified by prefixes, suffixes, and infixes. Standard tokenizers often struggle with this, sometimes splitting a single Hebrew word into many inefficient sub-tokens. 

2. Grammar and Syntax

• Statistical Approximation: LLMs do not "know" Hebrew grammar rules like a linguist; instead, they approximate grammar based on patterns found in billions of training examples.
• Contextual Relationships: Through training, the model learns the "normative expectations" of Hebrew text, such as which words frequently follow others. This allows it to generate grammatically correct sentences without explicit rule-based instruction. 

3. RTL Display and Formatting

• Rendering vs. Logic: The RTL aspect is primarily a display concern handled by the user interface (using the Unicode Bidirectional Algorithm). The model itself just predicts the next token in the sequence.
• Corruption Risks: Issues can arise during "mixed" text scenarios (e.g., Hebrew mixed with English or math symbols), where the directionality might confuse the model's output formatting, leading to errors in numeric values or symbol placement. 

4. Specialized Hebrew Models

Because generic models often have limited Hebrew training data, specialized models like DictaLM 2.0 have been developed. These models use: 

• Enhanced Vocabularies: Tokenizers specifically optimized for Hebrew's root-based structure.
• Hebrew-Centric Training: Training on massive Hebrew corpora (like DictaLM's 200 billion tokens) to better capture linguistic nuances. 

Would you like to see a comparison of how different LLMs (like GPT-4 vs. DictaLM) handle specific Hebrew grammar tasks?

1. [Reproduced this ai_mode answer as it appeared…] 
2. [Used paragraph form of blogger instead of normal form that I usually employ…] 
3. [The formatting is extensive with many lines of code and data…]
4. [Hoping to preserve links provided by ai_mode, but that doesn't happen…]
5. [Internal links are preserved, for example Adapting LLMs to Hebrew…]
6. [Interesting tidbit from Hacker News link above (LLMs highlighted): "Arguably Arabic numbers must always be read right-to-left, even in English, because the least significant digits can be read in order, while the value of the most significant digit depends on the number of less significant digits to the right. So in Hebrew the general reading direction actually fits Arabic numbers better…"]