Datasets:
Tasks:
Question Answering
Modalities:
Text
Formats:
json
Size:
1K - 10K
ArXiv:
Tags:
lost-in-the-middle
License:
File size: 1,715 Bytes
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---
license: apache-2.0
configs:
- config_name: kv75
data_files:
- split: test
path: "data/kv75.jsonl"
- config_name: kv140
data_files:
- split: test
path: "data/kv140.jsonl"
- config_name: kv300
data_files:
- split: test
path: "data/kv300.jsonl"
- config_name: qa10
data_files:
- split: test
path: "data/qa10.jsonl"
- config_name: qa20
data_files:
- split: test
path: "data/qa20.jsonl"
- config_name: qa30
data_files:
- split: test
path: "data/qa30.jsonl"
task_categories:
- question-answering
tags:
- lost-in-the-middle
size_categories:
- n<1K
---
# Datasets for Lost In The Middle
This repository contains datasets used in the paper ["Lost in the Middle: How Language Models Use Long Contexts"](https://arxiv.org/abs/2307.03172), focusing on multi-document question answering and key-value retrieval tasks.
## Datasets Overview
The datasets provided are as follows:
- **Key-Value Retrieval Datasets**
- `kv75`: Key-Value pairs with 75 keys.
- `kv140`: Key-Value pairs with 140 keys.
- `kv300`: Key-Value pairs with 300 keys.
- **Multi-Document Question Answering Datasets**
- `qa10`: Questions with answers derived from 10 documents.
- `qa20`: Questions with answers derived from 20 documents.
- `qa30`: Questions with answers derived from 30 documents.
## Loading the Data
You can load these datasets using the Hugging Face `datasets` library:
```python
from datasets import load_dataset
### Example for loading the kv75 dataset
dataset = load_dataset("bzantium/LITM", "kv75")
### Example for loading the qa20 dataset
dataset = load_dataset("bzantium/LITM", "qa20")
``` |