license: other
dataset_info:
- config_name: 2WikiMultihopQA
features:
- name: _id
dtype: string
- name: type
dtype: string
- name: question
dtype: string
- name: context
sequence:
- name: title
dtype: string
- name: content
sequence: string
- name: supporting_facts
sequence:
- name: title
dtype: string
- name: sent_id
dtype: int32
- name: evidences
sequence:
- name: fact
dtype: string
- name: relation
dtype: string
- name: entity
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 662142981
num_examples: 167454
- name: dev
num_bytes: 54346346
num_examples: 12576
- name: test
num_bytes: 51639331
num_examples: 12576
download_size: 389826062
dataset_size: 768128658
- config_name: MuSiQue
features:
- name: id
dtype: string
- name: paragraphs
list:
- name: idx
dtype: int64
- name: title
dtype: string
- name: paragraph_text
dtype: string
- name: is_supporting
dtype: bool
- name: question
dtype: string
- name: question_decomposition
list:
- name: id
dtype: int64
- name: question
dtype: string
- name: answer
dtype: string
- name: paragraph_support_idx
dtype: int64
- name: answer
dtype: string
- name: answer_aliases
sequence: string
- name: answerable
dtype: bool
- name: text_all
dtype: string
- name: text_all_support
dtype: string
splits:
- name: validation
num_bytes: 55971326
num_examples: 2417
download_size: 23776203
dataset_size: 55971326
- config_name: NQ
features:
- name: id
dtype: string
- name: title
dtype: string
- name: document
dtype: string
- name: question
dtype: string
- name: long_answers
sequence: string
- name: short_answers
sequence: string
- name: retrieved_passages
sequence: string
splits:
- name: validation
num_bytes: 279214996
num_examples: 4289
download_size: 141438208
dataset_size: 279214996
- config_name: hotpotqa
features:
- name: id
dtype: string
- name: question
dtype: string
- name: answer
dtype: string
- name: type
dtype: string
- name: level
dtype: string
- name: supporting_facts
sequence:
- name: title
dtype: string
- name: sent_id
dtype: int32
- name: context
sequence:
- name: title
dtype: string
- name: sentences
sequence: string
- name: rag
sequence: string
- name: retrieved_passages
sequence: string
splits:
- name: validation
num_bytes: 131225660
num_examples: 7405
download_size: 77113296
dataset_size: 131225660
- config_name: triviaqa
features:
- name: question
dtype: string
- name: question_id
dtype: string
- name: question_source
dtype: string
- name: entity_pages
sequence:
- name: doc_source
dtype: string
- name: filename
dtype: string
- name: title
dtype: string
- name: wiki_context
dtype: string
- name: search_results
sequence:
- name: description
dtype: string
- name: filename
dtype: string
- name: rank
dtype: int32
- name: title
dtype: string
- name: url
dtype: string
- name: search_context
dtype: string
- name: answer
struct:
- name: aliases
sequence: string
- name: normalized_aliases
sequence: string
- name: matched_wiki_entity_name
dtype: string
- name: normalized_matched_wiki_entity_name
dtype: string
- name: normalized_value
dtype: string
- name: type
dtype: string
- name: value
dtype: string
- name: retrieved_passages
sequence: string
splits:
- name: validation
num_bytes: 474767227
num_examples: 7993
download_size: 262352984
dataset_size: 474767227
- config_name: truthfulqa
features:
- name: question
dtype: string
- name: mc1_targets
struct:
- name: choices
sequence: string
- name: labels
sequence: int32
- name: mc2_targets
struct:
- name: choices
sequence: string
- name: labels
sequence: int32
- name: category
dtype: string
- name: source
dtype: string
- name: website_data
dtype: string
- name: retrieved_passages
sequence: string
splits:
- name: validation
num_bytes: 24476993
num_examples: 817
download_size: 10176147
dataset_size: 24476993
configs:
- config_name: 2WikiMultihopQA
data_files:
- split: train
path: 2WikiMultihopQA/train-*
- split: dev
path: 2WikiMultihopQA/dev-*
- split: test
path: 2WikiMultihopQA/test-*
- config_name: MuSiQue
data_files:
- split: validation
path: MuSiQue/validation-*
- config_name: NQ
data_files:
- split: validation
path: NQ/validation-*
- config_name: boolq
data_files:
- split: validation
path: boolq/validation-*
- config_name: hotpotqa
data_files:
- split: validation
path: hotpotqa/validation-*
- config_name: triviaqa
data_files:
- split: validation
path: triviaqa/validation-*
- config_name: truthfulqa
data_files:
- split: validation
path: truthfulqa/validation-*
ContextualBench - A comprehensive toolkit to evaluate LM on different Contextual datasets
Evaluation Code: SalesforceAIResearch/SFR-RAG
Description
ContextualBench is a powerful evaluation framework designed to assess the performance of Large Language Models (LLMs) on contextual datasets. It provides a flexible pipeline for evaluating various LLM families across different tasks, with a focus on handling large context inputs.
Users need to make their own assessment regarding any obligations or responsibilities under the corresponding licenses or terms and conditions pertaining to the original datasets and data.
Features
- Dynamic Retrieval Support: Efficiently handles large context inputs, allowing for comprehensive evaluation of LLMs' contextual understanding capabilities.
- Extensive Evaluation Dataset: Supports 7 contextual tasks, including: Question Answering (QA), Multi-Hop Question Answering, Classification tasks
- Multi-LLM Family Support: Compatible with a wide range of LLM families, including: Hugging Face models, Gemma, Mistral, OpenAI, Cohere.
Component Datasets of ContextualBench
Users need to make their own assessment regarding any obligations or responsibilities under the corresponding licenses or terms and conditions pertaining to the original datasets and data.
2WikiHotpotQA
This dataset is a multihop question answering task, as proposed in "Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps" by Ho. et. al The folder contains evaluation script and path to dataset on the validation split on around 12k samples.
@inproceedings{xanh2020_2wikimultihop,
title = "Constructing A Multi-hop {QA} Dataset for Comprehensive Evaluation of Reasoning Steps",
author = "Ho, Xanh and
Duong Nguyen, Anh-Khoa and
Sugawara, Saku and
Aizawa, Akiko",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.coling-main.580",
pages = "6609--6625",
}
HotpotQA
HotpotQA is a Wikipedia-based question-answer pairs with the questions require finding and reasoning over multiple supporting documents to answer. We evaluate on 7405 datapoints, on the distractor setting. This dataset was proposed in the below paper
@inproceedings{yang2018hotpotqa,
title={{HotpotQA}: A Dataset for Diverse, Explainable Multi-hop Question Answering},
author={Yang, Zhilin and Qi, Peng and Zhang, Saizheng and Bengio, Yoshua and Cohen, William W. and Salakhutdinov, Ruslan and Manning, Christopher D.},
booktitle={Conference on Empirical Methods in Natural Language Processing ({EMNLP})},
year={2018}
}
MuSiQue
This dataset is a multihop question answering task, that requires 2-4 hop in every questions, making it slightly harder task when compared to other multihop tasks.This dataset was proposed in the below paper
@article{trivedi2021musique,
title={{M}u{S}i{Q}ue: Multihop Questions via Single-hop Question Composition},
author={Trivedi, Harsh and Balasubramanian, Niranjan and Khot, Tushar and Sabharwal, Ashish},
journal={Transactions of the Association for Computational Linguistics},
year={2022}
publisher={MIT Press}
}
NaturalQuestions
The NQ corpus contains questions from real users, and it requires QA systems to read and comprehend an entire Wikipedia article that may or may not contain the answer to the question
@article{47761,
title = {Natural Questions: a Benchmark for Question Answering Research},
author = {Tom Kwiatkowski and Jennimaria Palomaki and Olivia Redfield and Michael Collins and Ankur Parikh and Chris Alberti and Danielle Epstein and Illia Polosukhin and Matthew Kelcey and Jacob Devlin and Kenton Lee and Kristina N. Toutanova and Llion Jones and Ming-Wei Chang and Andrew Dai and Jakob Uszkoreit and Quoc Le and Slav Petrov},
year = {2019},
journal = {Transactions of the Association of Computational Linguistics}
}
PopQA
PopQA is a large-scale open-domain question answering (QA) dataset, the long-tail subset, consisting of 1,399 rare entity queries whose monthly Wikipedia page views are less than 100
Make sure to cite the work
@article{ mallen2023llm_memorization ,
title={When Not to Trust Language Models: Investigating Effectiveness and Limitations of Parametric and Non-Parametric Memories },
author={ Mallen, Alex and Asai,Akari and Zhong, Victor and Das, Rajarshi and Hajishirzi, Hannaneh and Khashabi, Daniel},
journal={ arXiv preprint },
year={ 2022 }
}
TriviaQA
TriviaqQA is a reading comprehension dataset containing question-answer pairs authored by trivia enthusiasts and independently gathered evidence documents, six per question on average, that provide high quality distant supervision for answering the questions.
@article{2017arXivtriviaqa,
author = {{Joshi}, Mandar and {Choi}, Eunsol and {Weld},
Daniel and {Zettlemoyer}, Luke},
title = "{triviaqa: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension}",
journal = {arXiv e-prints},
year = 2017,
eid = {arXiv:1705.03551},
pages = {arXiv:1705.03551},
archivePrefix = {arXiv},
eprint = {1705.03551},
}
TruthfulQA
TruthfulQA is a benchmark to measure whether a language model is truthful in generating answers to questions. The benchmark comprises 817 questions that span 38 categories, including health, law, finance and politics. Questions are crafted so that some humans would answer falsely due to a false belief or misconception. To perform well, models must avoid generating false answers learned from imitating human texts.
@misc{lin2021truthfulqa,
title={TruthfulQA: Measuring How Models Mimic Human Falsehoods},
author={Stephanie Lin and Jacob Hilton and Owain Evans},
year={2021},
eprint={2109.07958},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
Citation
@article{nguyen2024sfrrag,
title={SFR-RAG: Towards Contextually Faithful LLMs},
author={Nguyen, Xuan-Phi and Pandit, Shrey and Purushwalkam, Senthil and Xu, Austin and Chen, Hailin and Ming, Yifei and Ke, Zixuan and Savarese, Silvio and Xong, Caiming and Joty, Shafiq},
year={2024}
}