README.md

---
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
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    num_examples: 4289
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  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
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  - 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
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    - 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
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    num_examples: 7993
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  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
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  - name: retrieved_passages
    sequence: string
  splits:
  - name: validation
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    num_examples: 817
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  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](https://github.com/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}
}

```