LOFT: A 1 Million+ Token Long-Context Benchmark

This repository houses the resources for LOFT, the Long Context Frontiers benchmark, introduced in the research paper Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More?. LOFT consists of 6 long-context task categories spanning retrieval, multi-hop compositional reasoning, and more, totaling 35 datasets and 4 modalities.

Installation

$ git clone git@github.com:google-deepmind/loft.git
$ cd loft/
$ pip install -r requirements.txt

Download Datasets and Prompts

The script below downloads all the LOFT datasets under BASE_DIR.

$ BASE_DIR=your-choice-of-directory
$ sh download.sh $BASE_DIR

Each dataset is also available from the links in the Datasets table. For a small subset, download.sh will additionally run preprocess.py, which infills the missing fields in the queries and corpus files. Once the download is completed, you will see the file structure as below:

$BASE_DIR
│
└───data
│   └───retrieval
│   │   └───arguana
│   │   │   └───32k
│   │   │   │   └───corpus.jsonl
│   │   │   │   └───dev_queries.jsonl
│   │   │   │   └───few_shot_queries.jsonl
│   │   │   └───128k
│   │   │   └───1m
│   │   └───fever
│   │   └───...
|   │
│   └───rag
│   └───sql
│   └───icl
│
└───prompts
    └───retrieval_128k
        │   retrieval_arguana_128k.txt
        │   retrieval_fever_128k.txt
        └───...

The data folder contains the LOFT datasets and the prompts folder contains samples of prompts used in LOFT. We also provide an example prompt in PROMPT_EXAMPLE.txt showing how Corpus-in-Context (CiC) prompting can be done for the text retrieval task.

Inference and Evaluation

We currently support using gemini-1.5-flash-002 from VertexAI for inference. Please prepare your PROJECT_ID from Google Cloud. To run the inference with gemini-1.5-flash-002 and evaluate predictions:

DATASET=msmarco
PROJECT_ID=your-gcp-project-id
python run_inference.py \
    --prompt_prefix_path ${BASE_DIR}/prompts/retrieval_128k/retrieval_${DATASET}_128k.txt \
    --data_dir ${BASE_DIR}/data/retrieval/${DATASET}/128k \
    --split dev \
    --context_length 128k \
    --output_path ${BASE_DIR}/outputs/retrieval/${DATASET}/128k/predictions.jsonl \
    --project_id ${PROJECT_ID}

python run_evaluation.py \
    --answer_file_path ${BASE_DIR}/data/retrieval/${DATASET}/128k/dev_queries.jsonl \
    --pred_file_path ${BASE_DIR}/outputs/retrieval/${DATASET}/128k/predictions.jsonl \
    --task_type retrieval

The same script can be found from infer_eval.sh. We provide example queries and predictions files in evaluation/example_predictions/. Each task_type outputs many different metric scores. To understand which task_type to use for each dataset and also to see the primary evaluation metric reported in the paper for each dataset, see the Datasets table.

Datasets

Task	Dataset	Description	Task Type	Primary Metric	Infilling Needed?	Download
Text Retrieval	ArguAna	Argument Retrieval	retrieval	recall@1	-	Link
Text Retrieval	FEVER	Fact Checking	retrieval	recall@1	-	Link
Text Retrieval	FIQA	Question Answering	retrieval	recall@1	✅	Link
Text Retrieval	MS MARCO	Web Search	retrieval	recall@1	✅	Link
Text Retrieval	NQ	Question Answering	retrieval	recall@1	-	Link
Text Retrieval	Quora	Duplication Detection	retrieval	recall@1	✅	Link
Text Retrieval	SciFact	Citation Prediction	retrieval	recall@1	-	Link
Text Retrieval	Touché-2020	Argument Retrieval	retrieval	recall@1	✅	Link
Text Retrieval	TopiOCQA	Multi-turn QA	retrieval	recall@1	-	Link
Text Retrieval	HotPotQA	Multi-hop QA	retrieval	mrecall@2	-	Link
Text Retrieval	MuSiQue	Multi-hop QA	retrieval	mrecall@5	-	Link
Text Retrieval	QAMPARI	Multi-target QA	retrieval	mrecall@5	-	Link
Text Retrieval	QUEST	Multi-target QA	retrieval	mrecall@3	-	Link
Visual Retrieval	Flickr30k	Image Retrieval	retrieval	recall@1	✅	Coming Soon
Visual Retrieval	MS COCO	Image Retrieval	retrieval	recall@1	✅	Coming Soon
Visual Retrieval	OVEN	Image-text Retrieval	retrieval	recall@1	-	Coming Soon
Visual Retrieval	MSR-VTT	Video Retrieval	retrieval	recall@1	✅	Link
Audio Retrieval	FLEURS-en	Audio Retrieval	retrieval	recall@1	-	Coming Soon
Audio Retrieval	FLEURS-es	Audio Retrieval	retrieval	recall@1	-	Coming Soon
Audio Retrieval	FLEURS-fr	Audio Retrieval	retrieval	recall@1	-	Coming Soon
Audio Retrieval	FLEURS-hi	Audio Retrieval	retrieval	recall@1	-	Coming Soon
Audio Retrieval	FLEURS-zh	Audio Retrieval	retrieval	recall@1	-	Coming Soon
RAG	NQ	Question Answering	rag	subspan_em	-	Link
RAG	TopiOCQA	Multi-turn QA	rag	subspan_em	-	Coming Soon
RAG	HotPotQA	Multi-hop QA	rag	subspan_em	-	Link
RAG	MuSiQue	Multi-hop QA	rag	subspan_em	-	Link
RAG	QAMPARI	Multi-target QA	multi_value_rag	subspan_em	-	Link
RAG	QUEST	Multi-target QA	multi_value_rag	subspan_em	-	Link
SQL	Spider	Single-turn SQL	sql	exec_acc	-	Link
SQL	SParC	Multi-turn SQL	sql	exec_acc	-	Link
Many-Shot ICL	BBH-date	Multiple-choice QA	icl	em	-	Link
Many-Shot ICL	BBH-salient	Multiple-choice QA	icl	em	-	Link
Many-Shot ICL	BBH-tracking7	Multiple-choice QA	icl	em	-	Link
Many-Shot ICL	BBH-web	Multiple-choice QA	icl	em	-	Link
Many-Shot ICL	LIB-dialogue	Classification	-	-	✅	Coming Soon

Past & Upcoming Releases

• Remaining multi-modal data.
• Prompts for RAG, SQL, and multi-modal retrieval.
• Prompt conversion code (data => prompt).
• Inference code and prompts for retrieval (10/25/24).
• Evaluation code for ICL and some ICL and visual retrieval datasets (8/30/24).
• Evaluation code for text tasks and code to regenerate some of the LOFT datasets (6/29/24).
• Initial release with links to download many of the LOFT text datasets (6/20/24).

Citing this work

@article{Lee2024LongContext,
  title={Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More?},
  author={Jinhyuk Lee and Anthony Chen and Zhuyun Dai and Dheeru Dua and Devendra Singh Sachan and Michael Boratko and Yi Luan and Sébastien M. R. Arnold and Vincent Perot and Siddharth Dalmia and Hexiang Hu and Xudong Lin and Panupong Pasupat and Aida Amini and Jeremy R. Cole and Sebastian Riedel and Iftekhar Naim and Ming-Wei Chang and Kelvin Guu},
  journal={ArXiv},
  year={2024},
  volume={abs/2406.13121},
  url={https://arxiv.org/abs/2406.13121}
}

License and disclaimer

Copyright 2024 DeepMind Technologies Limited

All software is licensed under the Apache License, Version 2.0 (Apache 2.0); you may not use this file except in compliance with the Apache 2.0 license. You may obtain a copy of the Apache 2.0 license at: https://www.apache.org/licenses/LICENSE-2.0

All other materials are licensed under the Creative Commons Attribution 4.0 International License (CC-BY). You may obtain a copy of the CC-BY license at: https://creativecommons.org/licenses/by/4.0/legalcode

Individual tasks may be subject to copyright and licensing from their respective owners - please see individual download files for details.

Unless required by applicable law or agreed to in writing, all software and materials distributed here under the Apache 2.0 or CC-BY licenses are distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the licenses for the specific language governing permissions and limitations under those licenses.

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