Most discussions about LLM performance focus on the model architecture and prompting. But there's a hidden factor: the tokenizer. It determines how much of your text fits in the context window.
One Ruler to Measure Them All: How Language Affects LLM Quality
Most discussions about LLM performance focus on the model architecture and prompting. But there's a hidden factor: the tokenizer. It determines how much of your text fits in the context window.
The Tokenizer Problem
Russian text consumes more tokens than English for the same information density. Some developers even switch to English prompts to save tokens and improve performance.
The Surprising Result
Learn how LLM tokenization works, why it drives cost and latency, and practical ways to reduce token usage in your AI apps with…
Researchers from Meta and Google built AutoTTS to automatically discover optimal LLM reasoning strategies, cutting token usage by…
A Blog post by Omar Kamali on Hugging Face
LLM-as-judge has become the dominant pattern for evaluating language model outputs. Tools like...
What do language models generate when you don't tell them what to generate? New research reveals that LLM families have distinct…
Understanding how to evaluate and benchmark Large Language Models (LLMS). Test, compare, and understand LLMs.
