---
language:
- en
dataset_info:
  features:
  - name: prompt
    dtype: string
  - name: answer
    dtype: string
  splits:
  - name: spell
    num_bytes: 465046
    num_examples: 1000
  - name: spell_inverse
    num_bytes: 446046
    num_examples: 1000
  - name: contains_char
    num_bytes: 424176
    num_examples: 1000
  - name: contains_word
    num_bytes: 530494
    num_examples: 1000
  - name: orth
    num_bytes: 607118
    num_examples: 1000
  - name: sem
    num_bytes: 608098
    num_examples: 1000
  - name: ins_char
    num_bytes: 560474
    num_examples: 1000
  - name: ins_word
    num_bytes: 775597
    num_examples: 1000
  - name: del_char
    num_bytes: 513247
    num_examples: 1000
  - name: del_word
    num_bytes: 689114
    num_examples: 1000
  - name: sub_char
    num_bytes: 532364
    num_examples: 1000
  - name: sub_word
    num_bytes: 743529
    num_examples: 1000
  - name: swap_char
    num_bytes: 470394
    num_examples: 1000
  - name: swap_word
    num_bytes: 675168
    num_examples: 1000
  download_size: 962103
  dataset_size: 8040865
configs:
- config_name: default
  data_files:
  - split: spell
    path: data/spell-*
  - split: spell_inverse
    path: data/spell_inverse-*
  - split: contains_char
    path: data/contains_char-*
  - split: contains_word
    path: data/contains_word-*
  - split: orth
    path: data/orth-*
  - split: sem
    path: data/sem-*
  - split: ins_char
    path: data/ins_char-*
  - split: ins_word
    path: data/ins_word-*
  - split: del_char
    path: data/del_char-*
  - split: del_word
    path: data/del_word-*
  - split: sub_char
    path: data/sub_char-*
  - split: sub_word
    path: data/sub_word-*
  - split: swap_char
    path: data/swap_char-*
  - split: swap_word
    path: data/swap_word-*
---

# CUTE

Here is the CUTE benchmark, a benchmark designed for testing LLM's ability to understand the characters within their tokens. 

To use this dataset as we did, make sure to use ``` tokenizer.apply_chat_template ``` on the prompt, and then add ``` Answer: " ``` afterwards to the resulting string. 
For smaller LLMs, you may need to do some post-processing on the final answer.

You can also check out our implementation at: https://github.com/Leukas/cute