Datasets:

f20180301
/

loft-rag-quest-128k

license: apache-2.0
task_categories:
  - question-answering
  - text-generation
language:
  - en
tags:
  - long-context
  - rag
  - retrieval-augmented-generation
  - loft
size_categories:
  - 1K<n<10K

LOFT RAG - Quest (128k)

Dataset Description

This dataset is part of the LOFT (Long-context Open Foundation Tasks) benchmark, specifically the RAG (Retrieval-Augmented Generation) task.

Dataset: Quest
Context Length: 128k
Task Type: RAG (Retrieval-Augmented Generation)
Language: English
Source: LOFT Benchmark (Google DeepMind)

Dataset Structure

Data Fields

context (string): Full prompt context including corpus documents and few-shot examples
question (string): Query separator + query format + query text
answer_prefix (string): Prefix for answer generation ("Final Answer: ")
answers (list[string]): Ground truth answers
task (string): Task identifier (e.g., "quest_128k")
max_new_tokens (int64): Maximum tokens for generation (256)

Data Splits

dev: Development set (10 examples)
test: Test set (100 examples)

Usage

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("loft-rag-quest-128k")

# Access splits
dev_data = dataset["dev"]
df_dev = dev_data.to_pandas()
test_data = dataset["test"]
df_test = test_data.to_pandas()

# Example usage
sample = dataset["dev"][0] if "dev" in dataset else dataset["test"][0]
context = sample["context"]
question = sample["question"]
answers = sample["answers"]

Dataset Creation

This dataset was converted from LOFT's original format to HuggingFace format using exact LOFT prompt construction to ensure 100% fidelity.

Prompt Construction: Uses LOFT's PromptRegistry and concatenate_chunks() for exact prompt matching
Few-shot Examples: Preserved exactly as in LOFT (5 examples)
Corpus Documents: Full corpus included in context (corpus-in-context approach)
Verification: All prompts verified to match LOFT originals exactly

Related Datasets

All LOFT RAG datasets are available under the loft-rag-* namespace:

Main Index - Overview of all datasets

Citation

@article{{loft2024,
  title={{LOFT: Long-context Open Foundation Tasks}},
  author={{Google DeepMind}},
  year={{2024}},
  url={{https://github.com/google-deepmind/loft}}
}}

License

Apache 2.0