README.md

metadata

license: apache-2.0
language:
  - th
  - en
tags:
  - meme
pretty_name: token_awareness
size_categories:
  - 1K<n<10K

Token Awareness Dataset

Built from "strawberry" meme 🍓, most LLM can't count the character of the word. Since this dataset is very easy to generate, we create a dataset for this benchmark just for fun.

For this dataset, we support only Thai (th) and English (en) language.

Dataset Creation

We sample words for each language from these sources:

• Thai: pythainlp's Thai words
• English: dwyl's English words

We then sample 500 word each weigted by the word score, which was computed using this simple heuristic: $$S(w)=\frac{\sqrt{\mathrm{\mid }w\mathrm{\mid }}}{\frac{1}{\mathrm{\mid }w\mathrm{\mid }}\sum _{i=1}^{\mathrm{\mid }w\mathrm{\mid }-1}\text{freq}(u_i)+\sum _{j=1}^{\mathrm{\mid }w\mathrm{\mid }-2}\text{freq}(b_j)}S(w)\; =\; \backslash frac\{\backslash sqrt\{|w|\}\}\{\backslash frac\{1\}\{|w|\}\; \backslash sum\_\{i=1\}^\{|w|-1\}\; \backslash text\{freq\}(u\_i)\; +\; \backslash sum\_\{j=1\}^\{|w|-2\}\; \backslash text\{freq\}(b\_j)\}$$

where:

• $|w|$ is the length of the word.
• $u_i$ represents the unigram (single character) in the word.
• $b_j$ represents the bigram (pair of consecutive characters) in the word.
• $\text{freq}(u_i)$ is the frequency of unigram $u_i$ in the overall corpus.
• $\text{freq}(b_j)$ is the frequency of bigram $b_j$ in the overall corpus.

we use a random seed of 42.

Author

Chompakorn Chaksangchaichot