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--- |
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license: apache-2.0 |
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task_categories: |
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- text-generation |
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language: |
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- en |
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tags: |
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- code |
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- python |
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- long-context |
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- coding |
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size_categories: |
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- 1K<n<10K |
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configs: |
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- config_name: 0k |
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data_files: |
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- split: test |
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path: data/0k/test/data-* |
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- split: train |
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path: data/0k/train/data-* |
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- split: validation |
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path: data/0k/validation/data-* |
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- split: prompt |
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path: data/0k/prompt/data-* |
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- config_name: 1k |
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data_files: |
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- split: test |
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path: data/1k/test/data-* |
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- split: train |
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path: data/1k/train/data-* |
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- split: validation |
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path: data/1k/validation/data-* |
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- split: prompt |
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path: data/1k/prompt/data-* |
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- config_name: 2k |
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data_files: |
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- split: test |
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path: data/2k/test/data-* |
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- split: train |
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path: data/2k/train/data-* |
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- split: validation |
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path: data/2k/validation/data-* |
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- split: prompt |
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path: data/2k/prompt/data-* |
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- config_name: 4k |
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data_files: |
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- split: test |
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path: data/4k/test/data-* |
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- split: train |
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path: data/4k/train/data-* |
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- split: validation |
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path: data/4k/validation/data-* |
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- split: prompt |
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path: data/4k/prompt/data-* |
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- config_name: 8k |
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data_files: |
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- split: test |
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path: data/8k/test/data-* |
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- split: train |
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path: data/8k/train/data-* |
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- split: validation |
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path: data/8k/validation/data-* |
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- split: prompt |
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path: data/8k/prompt/data-* |
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- config_name: 16k |
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data_files: |
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- split: test |
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path: data/16k/test/data-* |
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- split: train |
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path: data/16k/train/data-* |
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- split: validation |
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path: data/16k/validation/data-* |
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- split: prompt |
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path: data/16k/prompt/data-* |
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- config_name: 32k |
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data_files: |
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- split: test |
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path: data/32k/test/data-* |
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- split: train |
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path: data/32k/train/data-* |
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- split: validation |
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path: data/32k/validation/data-* |
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- split: prompt |
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path: data/32k/prompt/data-* |
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- config_name: 64k |
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data_files: |
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- split: test |
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path: data/64k/test/data-* |
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- split: train |
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path: data/64k/train/data-* |
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- split: validation |
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path: data/64k/validation/data-* |
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- split: prompt |
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path: data/64k/prompt/data-* |
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- config_name: 128k |
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data_files: |
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- split: test |
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path: data/128k/test/data-* |
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- split: train |
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path: data/128k/train/data-* |
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- split: validation |
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path: data/128k/validation/data-* |
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- split: prompt |
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path: data/128k/prompt/data-* |
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- config_name: 196k |
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data_files: |
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- split: test |
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path: data/196k/test/data-* |
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- split: train |
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path: data/196k/train/data-* |
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- split: validation |
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path: data/196k/validation/data-* |
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- split: prompt |
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path: data/196k/prompt/data-* |
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- config_name: 256k |
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data_files: |
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- split: test |
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path: data/256k/test/data-* |
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- split: train |
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path: data/256k/train/data-* |
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- split: validation |
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path: data/256k/validation/data-* |
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- split: prompt |
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path: data/256k/prompt/data-* |
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- config_name: 512k |
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data_files: |
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- split: test |
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path: data/512k/test/data-* |
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- split: train |
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path: data/512k/train/data-* |
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- split: validation |
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path: data/512k/validation/data-* |
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- split: prompt |
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path: data/512k/prompt/data-* |
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- config_name: 1m |
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data_files: |
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- split: test |
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path: data/1m/test/data-* |
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- split: train |
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path: data/1m/train/data-* |
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- split: validation |
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path: data/1m/validation/data-* |
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- split: prompt |
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path: data/1m/prompt/data-* |
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dataset_info: |
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features: |
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- name: task_id |
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dtype: int64 |
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- name: text |
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dtype: string |
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- name: code |
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dtype: string |
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- name: test_list |
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sequence: string |
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- name: test_setup_code |
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dtype: string |
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- name: challenge_test_list |
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sequence: string |
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- name: context |
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dtype: string |
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- name: context_id |
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dtype: string |
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- name: context_length_tokens |
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dtype: int64 |
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- name: code_length_chars |
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dtype: int64 |
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- name: dataset_version |
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dtype: string |
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splits: |
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- name: test |
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num_examples: 500 |
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- name: train |
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num_examples: 374 |
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- name: validation |
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num_examples: 90 |
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- name: prompt |
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num_examples: 10 |
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--- |
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# MBPP Long-Context Dataset |
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## Overview |
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MBPP Long-Context is a benchmark dataset that combines coding problems from the [MBPP (Mostly Basic Python Problems)](https://github.com/google-research/google-research/tree/master/mbpp) dataset with long-context distractors from [BABILong](https://github.com/booydar/babilong). This dataset evaluates code generation performance under long-context conditions, testing whether models can maintain coding ability with stuffed context. |
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## Dataset Structure |
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### Data Fields |
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Each sample contains: |
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#### Original MBPP Fields |
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- `task_id` (int): Unique task identifier |
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- `text` (str): Problem description |
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- `code` (str): Reference solution |
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- `test_list` (List[str]): Test cases (assertions) |
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- `test_setup_code` (str): Optional setup code |
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- `challenge_test_list` (List[str]): Additional test cases |
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#### Long-Context Fields |
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- `context` (str): Prepended distractor text from BABILong, ranging from 0k to 1M. |
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- `context_id` (str): BABILong source identifier (e.g., "babilong_128k_qa1_sample_42") |
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- `context_length_tokens` (int): Token count using Llama tokenizer |
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#### Metadata |
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- `code_length_chars` (int): Reference solution length for difficulty tracking |
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### Data Splits |
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All configurations follow the original MBPP split structure: |
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- **test**: 500 samples (primary evaluation set) |
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- **train**: 374 samples |
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- **validation**: 90 samples |
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- **prompt**: 10 samples (few-shot examples) |
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## Creating the dataset |
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To avoid confounding variables, this dataset uses stratified random assignment, where: |
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1. Sort MBPP tasks by code length |
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2. Get text from BABILong qa1-qa10 splits |
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3. Duplicate contexts to match task count (974 samples) |
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4. Shuffle contexts and assign to sorted tasks |
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## Source Datasets |
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### MBPP (Mostly Basic Python Problems) |
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- **Source**: [google-research-datasets/mbpp](https://huggingface.co/datasets/google-research-datasets/mbpp) |
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- **Size**: 974 problems |
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- **Paper**: [Program Synthesis with Large Language Models](https://arxiv.org/abs/2108.07732) |
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### BABILong |
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- **Source**: [RMT-team/babilong](https://huggingface.co/datasets/RMT-team/babilong) |
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- **Content**: `input` field from qa1-qa10 splits |
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- **Paper**: [BABILong: Testing the Limits of LLMs with Long Context Reasoning-in-a-Haystack](https://arxiv.org/abs/2406.10149) |
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