
The instructions and demonstrations for building formal logical reasoning capable Generative Large Language models. CoT rationales are generated with the GPT-4 API.

> For non-commercial research purposes only.

Update: Our updated paper has been accepted by the findings of EMNLP2023.

The dataset is hosted on the Huggingface Datasets. It is the only distribution channel we currently allow. **You can download data examples from our Github [Link](https://github.com/csitfun/LogiCoT)**

**Important**: To request the dataset, please 
1. Submit an access request through your huggingface account.
2. Send an email to Hanmeng Liu at hanhaishiyi@gmail.com. Please tell us your huggingface account username, your real name, org, and purpose. You need to guarantee that you will not share the data with others. We will approve your request after your info is provided.

`general_inference.jsonl`: English instruction tuning data for the general inference task

`general_inference_pruned`: a pruned version with a smaller size while more diverse

`mrc.jsonl`: English instruction tuning data for the logical reading comprehension task 

`mrc_zh.jsonl`: Chinese instruction tuning data for the logical reading comprehension task 

`entailmentbank.jsonl`: derived from the EntailmentBank data

`folio2instruction.jsonl`: derived from the FOLIO data


For more information, please refer to our preview Arxiv eprint paper - [LogiCoT: Logical Chain-of-Thought Instruction-tuning Data Collection with GPT-4](https://arxiv.org/abs/2305.12147)

## Seminal Data
* LogicInference
* EntailmentBank
* FOLIO
* ReClor
* LogiQA
## Instruction types
### General inference task
* Language to Logic
* One-Step Inference
* Inference Chains
### Multi-choice reading comprehension task
* Identify the Necessary Claim
* Strengthen an Argument
* Weaken an Argument
* Resolve a Situation
* Identify a Flaw in Arguments Reasoning

## How to cite
```
@inproceedings{liu2023logicot,
  title={LogiCoT: Logical Chain-of-Thought Instruction Tuning},
  author={Liu, Hanmeng and Teng, Zhiyang and Cui, Leyang and Zhang, Chaoli and Zhou, Qiji and Zhang, Yue},
  booktitle={Findings of the Association for Computational Linguistics: EMNLP 2023},
  pages={2908--2921},
  year={2023}
}
```
task_categories:text-generation size_categories:100K<n<1M language:en language:zh license:cc-by-nc-nd-4.0 instruction-finetuning arxiv:2305.12147 region:us
{"language": ["en", "zh"], "license": "cc-by-nc-nd-4.0", "size_categories": ["100K<n<1M"], "task_categories": ["text-generation"], "pretty_name": "logicot", "tags": ["instruction-finetuning"]}
TAGS
#task_categories-text-generation #size_categories-100K<n<1M #language-English #language-Chinese #license-cc-by-nc-nd-4.0 #instruction-finetuning #arxiv-2305.12147 #region-us 


The instructions and demonstrations for building formal logical reasoning capable Generative Large Language models. CoT rationales are generated with the GPT-4 API.

> For non-commercial research purposes only.

Update: Our updated paper has been accepted by the findings of EMNLP2023.

The dataset is hosted on the Huggingface Datasets. It is the only distribution channel we currently allow. You can download data examples from our Github Link

Important: To request the dataset, please 
1. Submit an access request through your huggingface account.
2. Send an email to Hanmeng Liu at hanhaishiyi@URL. Please tell us your huggingface account username, your real name, org, and purpose. You need to guarantee that you will not share the data with others. We will approve your request after your info is provided.

'general_inference.jsonl': English instruction tuning data for the general inference task

'general_inference_pruned': a pruned version with a smaller size while more diverse

'URL': English instruction tuning data for the logical reading comprehension task 

'mrc_zh.jsonl': Chinese instruction tuning data for the logical reading comprehension task 

'URL': derived from the EntailmentBank data

'URL': derived from the FOLIO data


For more information, please refer to our preview Arxiv eprint paper - LogiCoT: Logical Chain-of-Thought Instruction-tuning Data Collection with GPT-4

## Seminal Data
* LogicInference
* EntailmentBank
* FOLIO
* ReClor
* LogiQA
## Instruction types
### General inference task
* Language to Logic
* One-Step Inference
* Inference Chains
### Multi-choice reading comprehension task
* Identify the Necessary Claim
* Strengthen an Argument
* Weaken an Argument
* Resolve a Situation
* Identify a Flaw in Arguments Reasoning

## How to cite

## Seminal Data
* LogicInference
* EntailmentBank
* FOLIO
* ReClor
* LogiQA ## Instruction types ### General inference task
* Language to Logic
* One-Step Inference
* Inference Chains ### Multi-choice reading comprehension task
* Identify the Necessary Claim
* Strengthen an Argument
* Weaken an Argument
* Resolve a Situation
* Identify a Flaw in Arguments Reasoning ## How to cite
TAGS
#task_categories-text-generation #size_categories-100K<n<1M #language-English #language-Chinese #license-cc-by-nc-nd-4.0 #instruction-finetuning #arxiv-2305.12147 #region-us 
 ## Seminal Data
* LogicInference
* EntailmentBank
* FOLIO
* ReClor
* LogiQA ## Instruction types ### General inference task
* Language to Logic
* One-Step Inference
* Inference Chains ### Multi-choice reading comprehension task
* Identify the Necessary Claim
* Strengthen an Argument
* Weaken an Argument
* Resolve a Situation
* Identify a Flaw in Arguments Reasoning ## How to cite
passage: TAGS
#task_categories-text-generation #size_categories-100K<n<1M #language-English #language-Chinese #license-cc-by-nc-nd-4.0 #instruction-finetuning #arxiv-2305.12147 #region-us 
## Seminal Data
* LogicInference
* EntailmentBank
* FOLIO
* ReClor
* LogiQA## Instruction types### General inference task
* Language to Logic
* One-Step Inference
* Inference Chains### Multi-choice reading comprehension task
* Identify the Necessary Claim
* Strengthen an Argument
* Weaken an Argument
* Resolve a Situation
* Identify a Flaw in Arguments Reasoning## How to cite