
# MALLS NL-FOL Pairs



## Dataset details

MALLS (large language **M**odel gener**A**ted natural-**L**anguage-to-first-order-**L**ogic pair**S**) 
consists of pairs of real-world natural language (NL) statements and the corresponding first-order logic (FOL) rules annotations.
All pairs are generated by prompting GPT-4 and processed to ensure the validity of the FOL rules.

MALLS-v0 consists of the original 34K NL-FOL pairs. We validate FOL rules in terms of syntactical correctness, but we did not conduct a rigorous alignment check on the pairs, 
meaning the FOL rule may not accurately reflect the meaning of the NL statement.

MALLS-v0.1 consists of 28K NL-FOL pairs that are filtered from v0. We manually checked the alignment for 1K samples and developed a filtering pipeline to filter 
the main dataset.

# Dataset Structure

- The file `MALLS-v0.json` consists of the 34K unfiltered pairs of the MALLS-v0 dataset.
- The files `MALLS-v0.1-train.json` and `MALLS-v0.1-test.json` consist of the 27K auto-verified pairs and the 1K human-verified pairs.
- We also provide `folio_parsed.json` which consists of 2K pairs collected and processed from the FOLIO datset.

Each entry in the file is a dictionary object of the following format
```
{
  'NL': <the NL statment>,
  'FOL': <the FOL rule>
}
```

**License:**
Attribution-NonCommercial 4.0 International.
Since the data are collected from GPT-4, it also abides by the policy of OpenAI: https://openai.com/policies/terms-of-use

## Using the Dataset

We use MALLS to finetune LLaMA models for NL-FOL translation, namely LogicLLaMA, which achieves GPT-4 level performance.

**Project Page**
https://github.com/gblackout/LogicLLaMA


## Intended use
**Primary intended uses:**
MALLS is intended to be used for research.

## Citation
```
@article{yang2023harnessing,
      title={Harnessing the Power of Large Language Models for Natural Language to First-Order Logic Translation}, 
      author={Yuan Yang and Siheng Xiong and Ali Payani and Ehsan Shareghi and Faramarz Fekri},
      journal={arXiv preprint arXiv:2305.15541},
      year={2023}
}
```
task_categories:text-generation size_categories:10K<n<100K language:en license:cc-by-nc-4.0 region:us
{"language": ["en"], "license": "cc-by-nc-4.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-generation"], "pretty_name": "MALLS NL-FOL Pairs 34K", "viewer": true}
TAGS
#task_categories-text-generation #size_categories-10K<n<100K #language-English #license-cc-by-nc-4.0 #region-us 


# MALLS NL-FOL Pairs



## Dataset details

MALLS (large language Model generAted natural-Language-to-first-order-Logic pairS) 
consists of pairs of real-world natural language (NL) statements and the corresponding first-order logic (FOL) rules annotations.
All pairs are generated by prompting GPT-4 and processed to ensure the validity of the FOL rules.

MALLS-v0 consists of the original 34K NL-FOL pairs. We validate FOL rules in terms of syntactical correctness, but we did not conduct a rigorous alignment check on the pairs, 
meaning the FOL rule may not accurately reflect the meaning of the NL statement.

MALLS-v0.1 consists of 28K NL-FOL pairs that are filtered from v0. We manually checked the alignment for 1K samples and developed a filtering pipeline to filter 
the main dataset.

# Dataset Structure

- The file 'URL' consists of the 34K unfiltered pairs of the MALLS-v0 dataset.
- The files 'URL' and 'URL' consist of the 27K auto-verified pairs and the 1K human-verified pairs.
- We also provide 'folio_parsed.json' which consists of 2K pairs collected and processed from the FOLIO datset.

Each entry in the file is a dictionary object of the following format


License:
Attribution-NonCommercial 4.0 International.
Since the data are collected from GPT-4, it also abides by the policy of OpenAI: URL

## Using the Dataset

We use MALLS to finetune LLaMA models for NL-FOL translation, namely LogicLLaMA, which achieves GPT-4 level performance.

Project Page
URL


## Intended use
Primary intended uses:
MALLS is intended to be used for research.


# MALLS NL-FOL Pairs ## Dataset details

MALLS (large language Model generAted natural-Language-to-first-order-Logic pairS) 
consists of pairs of real-world natural language (NL) statements and the corresponding first-order logic (FOL) rules annotations.
All pairs are generated by prompting GPT-4 and processed to ensure the validity of the FOL rules.

MALLS-v0 consists of the original 34K NL-FOL pairs. We validate FOL rules in terms of syntactical correctness, but we did not conduct a rigorous alignment check on the pairs, 
meaning the FOL rule may not accurately reflect the meaning of the NL statement.

MALLS-v0.1 consists of 28K NL-FOL pairs that are filtered from v0. We manually checked the alignment for 1K samples and developed a filtering pipeline to filter 
the main dataset. # Dataset Structure

- The file 'URL' consists of the 34K unfiltered pairs of the MALLS-v0 dataset.
- The files 'URL' and 'URL' consist of the 27K auto-verified pairs and the 1K human-verified pairs.
- We also provide 'folio_parsed.json' which consists of 2K pairs collected and processed from the FOLIO datset.

Each entry in the file is a dictionary object of the following format


License:
Attribution-NonCommercial 4.0 International.
Since the data are collected from GPT-4, it also abides by the policy of OpenAI: URL ## Using the Dataset

We use MALLS to finetune LLaMA models for NL-FOL translation, namely LogicLLaMA, which achieves GPT-4 level performance.

Project Page
URL ## Intended use
Primary intended uses:
MALLS is intended to be used for research.
TAGS
#task_categories-text-generation #size_categories-10K<n<100K #language-English #license-cc-by-nc-4.0 #region-us 
 # MALLS NL-FOL Pairs ## Dataset details

MALLS (large language Model generAted natural-Language-to-first-order-Logic pairS) 
consists of pairs of real-world natural language (NL) statements and the corresponding first-order logic (FOL) rules annotations.
All pairs are generated by prompting GPT-4 and processed to ensure the validity of the FOL rules.

MALLS-v0 consists of the original 34K NL-FOL pairs. We validate FOL rules in terms of syntactical correctness, but we did not conduct a rigorous alignment check on the pairs, 
meaning the FOL rule may not accurately reflect the meaning of the NL statement.

MALLS-v0.1 consists of 28K NL-FOL pairs that are filtered from v0. We manually checked the alignment for 1K samples and developed a filtering pipeline to filter 
the main dataset. # Dataset Structure

- The file 'URL' consists of the 34K unfiltered pairs of the MALLS-v0 dataset.
- The files 'URL' and 'URL' consist of the 27K auto-verified pairs and the 1K human-verified pairs.
- We also provide 'folio_parsed.json' which consists of 2K pairs collected and processed from the FOLIO datset.

Each entry in the file is a dictionary object of the following format


License:
Attribution-NonCommercial 4.0 International.
Since the data are collected from GPT-4, it also abides by the policy of OpenAI: URL ## Using the Dataset

We use MALLS to finetune LLaMA models for NL-FOL translation, namely LogicLLaMA, which achieves GPT-4 level performance.

Project Page
URL ## Intended use
Primary intended uses:
MALLS is intended to be used for research.
passage: TAGS
#task_categories-text-generation #size_categories-10K<n<100K #language-English #license-cc-by-nc-4.0 #region-us 
# MALLS NL-FOL Pairs## Dataset details

MALLS (large language Model generAted natural-Language-to-first-order-Logic pairS) 
consists of pairs of real-world natural language (NL) statements and the corresponding first-order logic (FOL) rules annotations.
All pairs are generated by prompting GPT-4 and processed to ensure the validity of the FOL rules.

MALLS-v0 consists of the original 34K NL-FOL pairs. We validate FOL rules in terms of syntactical correctness, but we did not conduct a rigorous alignment check on the pairs, 
meaning the FOL rule may not accurately reflect the meaning of the NL statement.

MALLS-v0.1 consists of 28K NL-FOL pairs that are filtered from v0. We manually checked the alignment for 1K samples and developed a filtering pipeline to filter 
the main dataset.# Dataset Structure

- The file 'URL' consists of the 34K unfiltered pairs of the MALLS-v0 dataset.
- The files 'URL' and 'URL' consist of the 27K auto-verified pairs and the 1K human-verified pairs.
- We also provide 'folio_parsed.json' which consists of 2K pairs collected and processed from the FOLIO datset.

Each entry in the file is a dictionary object of the following format


License:
Attribution-NonCommercial 4.0 International.
Since the data are collected from GPT-4, it also abides by the policy of OpenAI: URL## Using the Dataset

We use MALLS to finetune LLaMA models for NL-FOL translation, namely LogicLLaMA, which achieves GPT-4 level performance.

Project Page
URL## Intended use
Primary intended uses:
MALLS is intended to be used for research.