A 246-file BIG-bench suite of string wrangling problems: infer a format change from few-shot pairs and emit the transformed string.
unassessed
| Category | reasoning |
|---|---|
| Subcategory | BIG-bench few-shot string-format transformation (dates, emails, names, phones, times, units, free text) |
| Page status | unknown |
| Metric | exact_str_match |
| Direction | higher_is_better |
| Unit | % |
| Dataset size | 7872 |
| Dataset licence | Apache-2.0 |
| Publisher | Universitat Politècnica de València (BIG-bench collaboration) |
mult_data_wrangling asks a model to finish a string transformation after seeing one or more input-output pairs from the same problem. Domains are dates, emails, free text, names, phones, times, and units (for example turning a compact date into hyphenated day-month-year, or taking the domain of an email). Authors at Universitat Politècnica de València built 123 problems and shipped each in two prompt styles (input-output and a short English statement). The skill is few-shot format induction on English-script strings, not SQL or table joins. It is not [entity_data_imputation](entity_data_imputation.md).
Free-text completion scored with exact_str_match. example_input_prefix "Input: ", example_output_prefix " Output: ", output_regex ("'.*?'") on the input-output style files. Keyword many-shot. Canary GUID embedded. Parent task.json has no examples; they live in 246 subtask files.
No model card in ModelSpec reports this benchmark yet.