# _RiddleSense_ ++: Evaluating LLMs' Riddling abilities

Some cleaning and other modifications to make [the riddle_sense dataset](https://huggingface.co/datasets/riddle_sense) more suitable for training language models to generate riddles via standard `text generation`

## Notable changes

- reformatting to use special tags indicating the question and answer components
- normalization: whitespace normalization, etc with `clean-text`
- **spell correction** the original dataset has numerous spelling errors; these are fixed using `BertChecker` from the `neuspell` library **for the questions only**. The answers were left as-is as there is some specific terminology/puns that get changed by SC.

## Notes & Gotchas

- the original dataset has a non-commercial clause, so this does too.

annotations_creators:crowdsourced language_creators:found multilinguality:monolingual size_categories:1K<n<10K source_datasets:riddle_sense language:en license:other riddles riddler non-commercial doi:10.57967/hf/0133 region:us
{"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["riddle_sense"], "pretty_name": "RiddleSense", "tags": ["riddles", "riddler", "non-commercial"]}
TAGS
#annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-riddle_sense #language-English #license-other #riddles #riddler #non-commercial #doi-10.57967/hf/0133 #region-us 

# _RiddleSense_ ++: Evaluating LLMs' Riddling abilities

Some cleaning and other modifications to make the riddle_sense dataset more suitable for training language models to generate riddles via standard 'text generation'

## Notable changes

- reformatting to use special tags indicating the question and answer components
- normalization: whitespace normalization, etc with 'clean-text'
- spell correction the original dataset has numerous spelling errors; these are fixed using 'BertChecker' from the 'neuspell' library for the questions only. The answers were left as-is as there is some specific terminology/puns that get changed by SC.

## Notes & Gotchas

- the original dataset has a non-commercial clause, so this does too.

# _RiddleSense_ ++: Evaluating LLMs' Riddling abilities

Some cleaning and other modifications to make the riddle_sense dataset more suitable for training language models to generate riddles via standard 'text generation' ## Notable changes

- reformatting to use special tags indicating the question and answer components
- normalization: whitespace normalization, etc with 'clean-text'
- spell correction the original dataset has numerous spelling errors; these are fixed using 'BertChecker' from the 'neuspell' library for the questions only. The answers were left as-is as there is some specific terminology/puns that get changed by SC. ## Notes & Gotchas

- the original dataset has a non-commercial clause, so this does too.
TAGS
#annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-riddle_sense #language-English #license-other #riddles #riddler #non-commercial #doi-10.57967/hf/0133 #region-us 
 # _RiddleSense_ ++: Evaluating LLMs' Riddling abilities

Some cleaning and other modifications to make the riddle_sense dataset more suitable for training language models to generate riddles via standard 'text generation' ## Notable changes

- reformatting to use special tags indicating the question and answer components
- normalization: whitespace normalization, etc with 'clean-text'
- spell correction the original dataset has numerous spelling errors; these are fixed using 'BertChecker' from the 'neuspell' library for the questions only. The answers were left as-is as there is some specific terminology/puns that get changed by SC. ## Notes & Gotchas

- the original dataset has a non-commercial clause, so this does too.