# Dataset Card for Algorithmic Reasoning (seed)

**Note: This dataset is WIP and most question's answer section is empty or incomplete! See also "Other Known Limitations" section**

**Warning: If you somehow do use this dataset, remember to NOT do any eval after training on the questions in this dataset!**

## Dataset Description

- **Homepage:** 
- **Repository:** 
- **Paper:** 
- **Leaderboard:** 
- **Point of Contact:** lemontea.Tom@gmail.com or https://github.com/lemonteaa

### Dataset Summary

Dataset to help LLM learn how to reason about code, especially on algorithmic tasks, by seeing human demostration.

### Supported Tasks and Leaderboards

[More Information Needed]

### Languages

[More Information Needed]

## Dataset Structure

### Data Instances

[More Information Needed]

### Data Fields

- Question title
- Question
- Thought - Internal thought process that reason step by step/in an organized manner
- Answer presented to user (proof or code) - with explanation if necessary

### Data Splits

No split as of now - all are in the training section.

## Dataset Creation

### Curation Rationale

[More Information Needed]

### Source Data

#### Initial Data Collection and Normalization

Questions are those I personally remember in my career, selected based on:

- interesting
- involving CS, math, or similar knowledge
- Target specific known weaknesses of existing open source/source available LLM (eg index notation handling)
- pratical/likely to appear in production work settings

#### Who are the source language producers?

[More Information Needed]

### Annotations

#### Annotation process

Manually created by me entirely, writing in a level of details exceeeding what usually appears on the internet (bootcamp/FANNG interview prep/leetcode style training website etc) to help AI/LLM access knowledge that may be too obvious to human to write down.

#### Who are the annotators?

[More Information Needed]

### Personal and Sensitive Information

None as they are general, objective knowledge.

## Considerations for Using the Data

### Social Impact of Dataset

Although it is doubtful this dataset can actually work, in the event it does this may result in enhancing coding capability of LLM (which is intended), but which may create downstream effect simply due to LLM capability enhancement.

### Discussion of Biases

As questions are selected partly based on my taste, areas in CS that I am not interested in may be underrepresented.

### Other Known Limitations

- While I try to cover various mainstream programming language, each problem target only one specific language.
- It is currently in free-style markdown file. Maybe could make a script to convert to more structured format.
- Questions are asked in a conversational tone instead of leetcode style with strict I/O specification, hence may be more suitable for human eval than automated eval (eg extract and run code output in sandbox against test case automatically).
- As the dataset is completely manually created by a single human, the dataset size is extremely small.

## Additional Information

### Dataset Curators

[More Information Needed]

### Licensing Information

MIT

### Citation Information

[More Information Needed]

### Contributions

[More Information Needed]
task_categories:text-generation task_categories:question-answering size_categories:n<1K language:en license:mit code region:us
{"language": ["en"], "license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-generation", "question-answering"], "tags": ["code"]}
TAGS
#task_categories-text-generation #task_categories-question-answering #size_categories-n<1K #language-English #license-mit #code #region-us 

# Dataset Card for Algorithmic Reasoning (seed)

Note: This dataset is WIP and most question's answer section is empty or incomplete! See also "Other Known Limitations" section

Warning: If you somehow do use this dataset, remember to NOT do any eval after training on the questions in this dataset!

## Dataset Description

- Homepage: 
- Repository: 
- Paper: 
- Leaderboard: 
- Point of Contact: lemontea.Tom@URL or URL

### Dataset Summary

Dataset to help LLM learn how to reason about code, especially on algorithmic tasks, by seeing human demostration.

### Supported Tasks and Leaderboards



### Languages



## Dataset Structure

### Data Instances



### Data Fields

- Question title
- Question
- Thought - Internal thought process that reason step by step/in an organized manner
- Answer presented to user (proof or code) - with explanation if necessary

### Data Splits

No split as of now - all are in the training section.

## Dataset Creation

### Curation Rationale



### Source Data

#### Initial Data Collection and Normalization

Questions are those I personally remember in my career, selected based on:

- interesting
- involving CS, math, or similar knowledge
- Target specific known weaknesses of existing open source/source available LLM (eg index notation handling)
- pratical/likely to appear in production work settings

#### Who are the source language producers?



### Annotations

#### Annotation process

Manually created by me entirely, writing in a level of details exceeeding what usually appears on the internet (bootcamp/FANNG interview prep/leetcode style training website etc) to help AI/LLM access knowledge that may be too obvious to human to write down.

#### Who are the annotators?



### Personal and Sensitive Information

None as they are general, objective knowledge.

## Considerations for Using the Data

### Social Impact of Dataset

Although it is doubtful this dataset can actually work, in the event it does this may result in enhancing coding capability of LLM (which is intended), but which may create downstream effect simply due to LLM capability enhancement.

### Discussion of Biases

As questions are selected partly based on my taste, areas in CS that I am not interested in may be underrepresented.

### Other Known Limitations

- While I try to cover various mainstream programming language, each problem target only one specific language.
- It is currently in free-style markdown file. Maybe could make a script to convert to more structured format.
- Questions are asked in a conversational tone instead of leetcode style with strict I/O specification, hence may be more suitable for human eval than automated eval (eg extract and run code output in sandbox against test case automatically).
- As the dataset is completely manually created by a single human, the dataset size is extremely small.

## Additional Information

### Dataset Curators



### Licensing Information

MIT





### Contributions


# Dataset Card for Algorithmic Reasoning (seed)

Note: This dataset is WIP and most question's answer section is empty or incomplete! See also "Other Known Limitations" section

Warning: If you somehow do use this dataset, remember to NOT do any eval after training on the questions in this dataset! ## Dataset Description

- Homepage: 
- Repository: 
- Paper: 
- Leaderboard: 
- Point of Contact: lemontea.Tom@URL or URL ### Dataset Summary

Dataset to help LLM learn how to reason about code, especially on algorithmic tasks, by seeing human demostration. ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields

- Question title
- Question
- Thought - Internal thought process that reason step by step/in an organized manner
- Answer presented to user (proof or code) - with explanation if necessary ### Data Splits

No split as of now - all are in the training section. ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization

Questions are those I personally remember in my career, selected based on:

- interesting
- involving CS, math, or similar knowledge
- Target specific known weaknesses of existing open source/source available LLM (eg index notation handling)
- pratical/likely to appear in production work settings #### Who are the source language producers? ### Annotations #### Annotation process

Manually created by me entirely, writing in a level of details exceeeding what usually appears on the internet (bootcamp/FANNG interview prep/leetcode style training website etc) to help AI/LLM access knowledge that may be too obvious to human to write down. #### Who are the annotators? ### Personal and Sensitive Information

None as they are general, objective knowledge. ## Considerations for Using the Data ### Social Impact of Dataset

Although it is doubtful this dataset can actually work, in the event it does this may result in enhancing coding capability of LLM (which is intended), but which may create downstream effect simply due to LLM capability enhancement. ### Discussion of Biases

As questions are selected partly based on my taste, areas in CS that I am not interested in may be underrepresented. ### Other Known Limitations

- While I try to cover various mainstream programming language, each problem target only one specific language.
- It is currently in free-style markdown file. Maybe could make a script to convert to more structured format.
- Questions are asked in a conversational tone instead of leetcode style with strict I/O specification, hence may be more suitable for human eval than automated eval (eg extract and run code output in sandbox against test case automatically).
- As the dataset is completely manually created by a single human, the dataset size is extremely small. ## Additional Information ### Dataset Curators ### Licensing Information

MIT ### Contributions
TAGS
#task_categories-text-generation #task_categories-question-answering #size_categories-n<1K #language-English #license-mit #code #region-us 
 # Dataset Card for Algorithmic Reasoning (seed)

Note: This dataset is WIP and most question's answer section is empty or incomplete! See also "Other Known Limitations" section

Warning: If you somehow do use this dataset, remember to NOT do any eval after training on the questions in this dataset! ## Dataset Description

- Homepage: 
- Repository: 
- Paper: 
- Leaderboard: 
- Point of Contact: lemontea.Tom@URL or URL ### Dataset Summary

Dataset to help LLM learn how to reason about code, especially on algorithmic tasks, by seeing human demostration. ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields

- Question title
- Question
- Thought - Internal thought process that reason step by step/in an organized manner
- Answer presented to user (proof or code) - with explanation if necessary ### Data Splits

No split as of now - all are in the training section. ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization

Questions are those I personally remember in my career, selected based on:

- interesting
- involving CS, math, or similar knowledge
- Target specific known weaknesses of existing open source/source available LLM (eg index notation handling)
- pratical/likely to appear in production work settings #### Who are the source language producers? ### Annotations #### Annotation process

Manually created by me entirely, writing in a level of details exceeeding what usually appears on the internet (bootcamp/FANNG interview prep/leetcode style training website etc) to help AI/LLM access knowledge that may be too obvious to human to write down. #### Who are the annotators? ### Personal and Sensitive Information

None as they are general, objective knowledge. ## Considerations for Using the Data ### Social Impact of Dataset

Although it is doubtful this dataset can actually work, in the event it does this may result in enhancing coding capability of LLM (which is intended), but which may create downstream effect simply due to LLM capability enhancement. ### Discussion of Biases

As questions are selected partly based on my taste, areas in CS that I am not interested in may be underrepresented. ### Other Known Limitations

- While I try to cover various mainstream programming language, each problem target only one specific language.
- It is currently in free-style markdown file. Maybe could make a script to convert to more structured format.
- Questions are asked in a conversational tone instead of leetcode style with strict I/O specification, hence may be more suitable for human eval than automated eval (eg extract and run code output in sandbox against test case automatically).
- As the dataset is completely manually created by a single human, the dataset size is extremely small. ## Additional Information ### Dataset Curators ### Licensing Information

MIT ### Contributions
passage: TAGS
#task_categories-text-generation #task_categories-question-answering #size_categories-n<1K #language-English #license-mit #code #region-us 
# Dataset Card for Algorithmic Reasoning (seed)

Note: This dataset is WIP and most question's answer section is empty or incomplete! See also "Other Known Limitations" section

Warning: If you somehow do use this dataset, remember to NOT do any eval after training on the questions in this dataset!## Dataset Description

- Homepage: 
- Repository: 
- Paper: 
- Leaderboard: 
- Point of Contact: lemontea.Tom@URL or URL### Dataset Summary

Dataset to help LLM learn how to reason about code, especially on algorithmic tasks, by seeing human demostration.### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields

- Question title
- Question
- Thought - Internal thought process that reason step by step/in an organized manner
- Answer presented to user (proof or code) - with explanation if necessary### Data Splits

No split as of now - all are in the training section.## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization

Questions are those I personally remember in my career, selected based on:

- interesting
- involving CS, math, or similar knowledge
- Target specific known weaknesses of existing open source/source available LLM (eg index notation handling)
- pratical/likely to appear in production work settings#### Who are the source language producers?### Annotations#### Annotation process

Manually created by me entirely, writing in a level of details exceeeding what usually appears on the internet (bootcamp/FANNG interview prep/leetcode style training website etc) to help AI/LLM access knowledge that may be too obvious to human to write down.#### Who are the annotators?### Personal and Sensitive Information

None as they are general, objective knowledge.## Considerations for Using the Data