McJackTang / LongBench

LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding

🤗 HF Repo • 📃 Paper coming soon!

阅读中文版本.

📖 LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding

LongBench is the first benchmark for bilingual, multitask, and comprehensive assessment of long context understanding capabilities of large language models. LongBench includes different languages (Chinese and English) to provide a more comprehensive evaluation of the large models' multilingual capabilities on long contexts. In addition, LongBench is composed of six major categories and twenty different tasks, covering key long-text application scenarios such as multi-document QA, single-document QA, summarization, Few-shot learning, code completion, and synthesis tasks.

We are fully aware of the potentially high costs involved in the model evaluation process, especially in the context of long context scenarios (such as manual annotation costs or API call costs). Therefore, we adopt a fully automated evaluation method, aimed at measuring and evaluating the model's ability to understand long contexts at the lowest cost.

LongBench includes 13 English tasks, 5 Chinese tasks, and 2 code tasks, with the average length of most tasks ranging from 5k to 15k. For detailed statistics and construction methods of LongBench tasks, please refer here.

Task Type	#English Task	#Chinese Task	#Code Task
Multi-document QA	3	1	-
Single-document QA	3	1	-
Summarization	2	1	-
Few-shot learning	3	1	-
Synthetic Tasks	2	1	-
Code Completion	-	-	2

🔍 Table of Contents

• 🖥️ Leaderboard
• ⚙️ How to evaluate on LongBench
• 📊 Evaluation Result on Each Dataset
• 📄 Acknowledgement
• 📝 Citation

🖥️ Leaderboard

Here is the average scores (%) on the main task categories in both Chinese and English languages under the Zero-shot scenario. Please refer to this link for the evaluation metrics used for each task.

Note: For text exceeding the processing length capability of the model, we truncate from the middle of the text, preserving information from the beginning and end, in accordance with the observations from Lost in the Middle. Experiments show that this truncation method has the least impact on model performance.

English

	Avg	Single-Doc QA	Multi-Doc QA	Summarization	Few-shot Learning	Code Completion	Synthetic Tasks
GPT-3.5-Turbo-16k	45.5	39.8	38.7	26.5	76.0	54.5	37.8
Llama2-7B-chat-4k	29.0	24.8	21.4	23.9	50.5	47.3	5.9
LongChat-7B-16k	33.7	29.3	16.1	25.8	59.9	57.0	14.2
XGen-7B-8k	28.7	24.5	20.4	24.8	58.7	38.0	5.6
InternLM-7B-8k	24.7	17.1	20.8	13.3	52.7	39.7	4.7
ChatGLM2-6B	26.0	23.1	15.0	22.9	46.1	46.1	2.7
ChatGLM2-6B-32k	42.7	32.8	34.0	28.6	68.1	52.7	39.8

Chinese

	Avg	Single-Doc QA	Multi-Doc QA	Summarization	Few-shot Learning	Code Completion	Synthetic Tasks
GPT-3.5-Turbo-16k	44.5	61.2	28.7	16.0	29.2	54.5	77.5
Llama2-7B-chat-4k	13.5	11.6	1.9	0.2	19.8	47.3	0.5
LongChat-7B-16k	23.7	26.6	19.1	14.0	20.8	57.0	4.8
XGen-7B-8k	14.5	14.2	9.1	1.5	20.0	38.0	4.2
InternLM-7B-8k	18.6	33.3	8.9	13.0	15.5	39.7	0.9
ChatGLM2-6B	22.5	33.0	15.2	14.6	20.5	46.1	5.5
ChatGLM2-6B-32k	41.3	52.0	34.3	16.3	29.9	52.7	62.5

Radar Chart on Long Context Capability

Variation of Abilities under Different Context Lengths

To more specifically analyze the models' relative performance under different context lengths, the following chart shows the average relative scores on all tasks over different context length intervals.

Note: Assume that the model scores x on the data within a specific length range of a task, and y on all data of that task, then the model's relative score for that length range is (x/y-1). To better compare the trends of different models, we shift all the lines to 0 on 0-4k.

⚙️ How to evaluate on LongBench

Load Data

You can download and load the LongBench data through the Hugging Face datasets (🤗 HF Repo):

from datasets import load_dataset

datasets = ["hotpotqa", "2wikimqa", "musique", "dureader", "narrativeqa", "qasper", "multifieldqa_en", \
    "multifieldqa_zh", "gov_report", "qmsum", "vcsum", "trec", "nq", "triviaqa", "lsht", "passage_count", \
    "passage_retrieval_en", "passage_retrieval_zh", "lcc", "repobench-p"]

for dataset in datasets:
    data = load_dataset('THUDM/LongBench', dataset, split='test')

Alternatively, you can download the folder from this link to load the data.

Data Format

All data in LongBench are standardized to the following format:

{
    "input": "The input/command for the task, usually short, such as questions in QA, queries in Few-shot tasks, etc",
    "context": "The long context required for the task, such as documents, cross-file code, few-shot examples in Few-shot tasks",
    "answers": "A List of all true answers",
    "length": "Total length of the first three items (counted in characters for Chinese and words for English)",
    "dataset": "The name of the dataset to which this piece of data belongs",
    "language": "The language of this piece of data",
    "all_classes": "All categories in classification tasks, null for non-classification tasks",
    "_id": "Random id for each piece of data"
}

Evaluation

Install the requirements with pip: pip install -r requirements.txt. We provide an evaluation code using ChatGLM2-6B as an example. First, run the pred.py under the repository:

CUDA_VISIBLE_DEVICES=0 python pred.py

You can get the model outputs on all datasets in the pred/ folder. After that, run the evaluation code in eval.py:

python eval.py

You can get the evaluation results on all datasets in result.json. Please note that in config/, we provide the input format suitable for each dataset and the maximum output length. Feel free to modify them to better suit the model you want to evaluate. After modification, when evaluating with pred.py, the data will be automatically organized according to the new format to get the corresponding model output.

📊 Evaluation Result on Each Dataset

The following tables show the Zero-shot evaluation results (%) on all datasets, where Chinese datasets are denoted by "zh" (please refer to this link for the evaluation metrics used for each task).

Single-Document QA

	NarrativeQA	Qasper	MultiFieldQA-en	MultiFieldQA-zh
GPT-3.5-Turbo-16k	23.6	43.3	52.3	61.2
Llama2-7B-chat-4k	19.1	19.6	35.8	11.6
LongChat-7B-16k	21.6	21.6	44.6	26.6
XGen-7B-8k	17.9	18.3	37.2	14.2
InternLM-7B-8k	12.4	16.8	22.3	33.3
ChatGLM2-6B	11.2	23.7	34.2	33.0
ChatGLM2-6B-32k	20.4	32.2	45.7	52.0

Multi-Document QA

	HotpotQA	2WikiMQA	Musique	DuReader (zh)
GPT-3.5-Turbo-16k	51.6	37.7	26.9	28.7
Llama2-7B-chat-4k	24.3	31.4	8.6	1.9
LongChat-7B-16k	22.4	16.8	9.1	19.1
XGen-7B-8k	28.3	21.5	11.5	9.1
InternLM-7B-8k	27.9	24.0	10.3	8.9
ChatGLM2-6B	20.2	19.6	5.3	15.2
ChatGLM2-6B-32k	44.9	34.9	22.2	34.3

Summarization

	GovReport	QMSum	VCSUM (zh)
GPT-3.5-Turbo-16k	29.5	23.4	16.0
Llama2-7B-chat-4k	27.3	20.6	0.2
LongChat-7B-16k	28.4	23.2	14.0
XGen-7B-8k	27.8	21.7	1.5
InternLM-7B-8k	9.8	16.8	13.0
ChatGLM2-6B	23.7	22.2	14.6
ChatGLM2-6B-32k	33.3	23.9	16.3

Few-shot Learning

	TREC	NQ	TriviaQA	LSHT (zh)
GPT-3.5-Turbo-16k	68.0	73.0	87.1	29.2
Llama2-7B-chat-4k	60.5	31.4	59.7	19.8
LongChat-7B-16k	61.5	44.8	73.5	20.8
XGen-7B-8k	66.0	43.2	67.0	20.0
InternLM-7B-8k	49.0	47.6	61.6	15.5
ChatGLM2-6B	44.0	34.5	59.8	20.5
ChatGLM2-6B-32k	62.0	64.9	77.6	29.9

Code Completion

	LCC	RepoBench-P
GPT-3.5-Turbo-16k	54.7	54.3
Llama2-7B-chat-4k	52.3	42.4
LongChat-7B-16k	59.2	54.7
XGen-7B-8k	38.8	37.3
InternLM-7B-8k	45.5	34.0
ChatGLM2-6B	48.4	43.7
ChatGLM2-6B-32k	55.4	50.0

Synthetic Tasks

	PassageRetrieval-en	Passage Count	PassageRetrieval-zh
GPT-3.5-Turbo-16k	71.0	4.5	77.5
Llama2-7B-chat-4k	9.2	2.5	0.5
LongChat-7B-16k	24.0	4.5	4.8
XGen-7B-8k	9.0	2.2	4.2
InternLM-7B-8k	6.5	2.9	0.9
ChatGLM2-6B	3.2	2.1	5.5
ChatGLM2-6B-32k	77.5	2.0	62.5

📄 Acknowledgement

• Some of the tasks of LongBench are based on the datasets proposed by previous researchers, including HotpotQA, 2WikiMultihopQA, Musique, DuReader, NarrativeQA, Qasper, GovReport, QMSum, VCSUM, TriviaQA, NQ, TREC, LSHT, LCC and RepoBench-P.

📝 Citation

This is a joint work by THU-KEG and Zhipu AI. We are currently working on the paper, and the citation information will be updated when it's ready. Please stay tuned~

When citing our work, please cite all of the original dataset papers. The relevant citation information is listed here.