Datasets:
language:
- en
license:
- other
size_categories:
- 1K<n<10K
tags:
- RAG
- ConvRAG
- conversational QA
- multi-turn QA
- QA with context
- evaluation
configs:
- config_name: coqa
data_files:
- split: dev
path: data/coqa/*
- config_name: inscit
data_files:
- split: dev
path: data/inscit/*
- config_name: inscit
data_files:
- split: dev
path: data/inscit/*
- config_name: topiocqa
data_files:
- split: dev
path: data/topiocqa/*
- config_name: hybridial
data_files:
- split: test
path: data/hybridial/*
- config_name: doc2dial
data_files:
- split: test
path: data/doc2dial/test.json
- config_name: quac
data_files:
- split: test
path: data/quac/test.json
- config_name: qrecc
data_files:
- split: test
path: data/qrecc/test.json
- config_name: doqa_cooking
data_files:
- split: test
path: data/doqa/test_cooking.json
- config_name: doqa_movies
data_files:
- split: test
path: data/doqa/test_movies.json
- config_name: doqa_travel
data_files:
- split: test
path: data/doqa/test_travel.json
- config_name: sqa
data_files:
- split: test
path: data/sqa/test.json
ConvRAG Bench
ConvRAG Bench is a benchmark for evaluating a model's conversational QA capability over documents or retrieved context. ConvRAG Bench are built on and derived from 10 existing datasets: Doc2Dial, QuAC, QReCC, TopioCQA, INSCIT, CoQA, HybriDialogue, DoQA, SQA, ConvFinQA. ConvRAG Bench covers a wide range of documents and question types, which require models to generate responses from long context, comprehend and reason over tables, conduct arithmetic calculations, and indicate when questions cannot be found within the context.
Benchmark Results
Main Results
| ChatQA-1.0-7B | Command-R-Plus | Llama-3-instruct-70b | GPT-4-0613 | ChatQA-1.0-70B | ChatQA-1.5-8B | ChatQA-1.5-70B | |
|---|---|---|---|---|---|---|---|
| Doc2Dial | 37.88 | 33.51 | 37.88 | 34.16 | 38.9 | 39.33 | 41.26 |
| QuAC | 29.69 | 34.16 | 36.96 | 40.29 | 41.82 | 39.73 | 38.82 |
| QReCC | 46.97 | 49.77 | 51.34 | 52.01 | 48.05 | 49.03 | 51.40 |
| CoQA | 76.61 | 69.71 | 76.98 | 77.42 | 78.57 | 76.46 | 78.44 |
| DoQA | 41.57 | 40.67 | 41.24 | 43.39 | 51.94 | 49.6 | 50.67 |
| ConvFinQA | 51.61 | 71.21 | 76.6 | 81.28 | 73.69 | 78.46 | 81.88 |
| SQA | 61.87 | 74.07 | 69.61 | 79.21 | 69.14 | 73.28 | 83.82 |
| TopioCQA | 45.45 | 53.77 | 49.72 | 45.09 | 50.98 | 49.96 | 55.63 |
| HybriDial* | 54.51 | 46.7 | 48.59 | 49.81 | 56.44 | 65.76 | 68.27 |
| INSCIT | 30.96 | 35.76 | 36.23 | 36.34 | 31.9 | 30.1 | 32.31 |
| Average (all) | 47.71 | 50.93 | 52.52 | 53.90 | 54.14 | 55.17 | 58.25 |
| Average (exclude HybriDial) | 46.96 | 51.40 | 52.95 | 54.35 | 53.89 | 53.99 | 57.14 |
Note that ChatQA-1.5 used some samples from the HybriDial training dataset. To ensure fair comparison, we also compare average scores excluding HybriDial.
Evaluation of Unanswerable Scenario
ConvRAG Bench also includes evaluations for the unanswerable scenario, where we evaluate models' capability to determine whether the answer to the question can be found within the given context. Equipping models with such capability can substantially decrease the likelihood of hallucination.
| GPT-3.5-turbo-0613 | Command-R-Plus | Llama-3-instruct-70b | GPT-4-0613 | ChatQA-1.0-70B | ChatQA-1.5-8B | ChatQA-1.5-70B | |
|---|---|---|---|---|---|---|---|
| Avg-Both | 73.27 | 68.11 | 76.42 | 80.73 | 77.25 | 75.57 | 71.86 |
| Avg-QuAC | 78.335 | 69.605 | 81.285 | 87.415 | 80.755 | 79.3 | 72.59 |
| QuAC (no*) | 61.91 | 41.79 | 66.89 | 83.45 | 77.66 | 63.39 | 48.25 |
| QuAC (yes*) | 94.76 | 97.42 | 95.68 | 91.38 | 83.85 | 95.21 | 96.93 |
| Avg-DoQA | 68.21 | 66.62 | 71.555 | 74.05 | 73.74 | 71.84 | 71.125 |
| DoQA (no*) | 51.99 | 46.37 | 60.78 | 74.28 | 68.81 | 62.76 | 52.24 |
| DoQA (yes*) | 84.43 | 86.87 | 82.33 | 73.82 | 78.67 | 80.92 | 90.01 |
We use QuAC and DoQA datasets which have such unanswerable cases to evaluate such capability. We use both answerable and unanswerable samples for this evaluation. Specifically, for unanswerable case, we consider the model indicating that the question cannot be answered as correct, and as for answerable cases, we consider the model not indicating the question is unanswerable as correct (i.e., the model giving an answer). In the end, we calculate the average accuracy score of unanswerable and answerable cases as the final metric.
Evaluation Scripts
We also open-source the scripts for running and evaluating on ConvRAG (including the unanswerable scenario evaluations).
License
The ConvRAG are built on and derived from existing datasets. We refer users to the original licenses accompanying each dataset.
Correspondence to
Zihan Liu (zihanl@nvidia.com), Wei Ping (wping@nvidia.com)
Citation
@article{liu2024chatqa,
title={ChatQA: Building GPT-4 Level Conversational QA Models},
author={Liu, Zihan and Ping, Wei and Roy, Rajarshi and Xu, Peng and Lee, Chankyu and Shoeybi, Mohammad and Catanzaro, Bryan},
journal={arXiv preprint arXiv:2401.10225},
year={2024}}
Acknowledgement
We would like to give credits to all the works constructing the datasets we use for evaluating ChatQA. If you use these resources, please also cite all the datasets you use.
@inproceedings{feng2020doc2dial,
title={doc2dial: A Goal-Oriented Document-Grounded Dialogue Dataset},
author={Feng, Song and Wan, Hui and Gunasekara, Chulaka and Patel, Siva and Joshi, Sachindra and Lastras, Luis},
booktitle={Proceedings of the 2020 Conference on EMNLP},
year={2020}
}
@inproceedings{choi2018quac,
title={QuAC: Question Answering in Context},
author={Choi, Eunsol and He, He and Iyyer, Mohit and Yatskar, Mark and Yih, Wen-tau and Choi, Yejin and Liang, Percy and Zettlemoyer, Luke},
booktitle={Proceedings of the 2018 Conference on EMNLP},
year={2018}
}
@inproceedings{anantha2021open,
title={Open-Domain Question Answering Goes Conversational via Question Rewriting},
author={Anantha, Raviteja and Vakulenko, Svitlana and Tu, Zhucheng and Longpre, Shayne and Pulman, Stephen and Chappidi, Srinivas},
booktitle={Proceedings of the 2021 Conference on NAACL},
year={2021}
}
@article{reddy2019coqa,
title={CoQA: A Conversational Question Answering Challenge},
author={Reddy, Siva and Chen, Danqi and Manning, Christopher D},
journal={Transactions of the Association for Computational Linguistics},
year={2019}
}
@inproceedings{campos2020doqa,
title={DoQA-Accessing Domain-Specific FAQs via Conversational QA},
author={Campos, Jon Ander and Otegi, Arantxa and Soroa, Aitor and Deriu, Jan Milan and Cieliebak, Mark and Agirre, Eneko},
booktitle={Proceedings of the 2020 Conference on ACL},
year={2020}
}
@inproceedings{chen2022convfinqa,
title={ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answering},
author={Chen, Zhiyu and Li, Shiyang and Smiley, Charese and Ma, Zhiqiang and Shah, Sameena and Wang, William Yang},
booktitle={Proceedings of the 2022 Conference on EMNLP},
year={2022}
}
@inproceedings{iyyer2017search,
title={Search-based neural structured learning for sequential question answering},
author={Iyyer, Mohit and Yih, Wen-tau and Chang, Ming-Wei},
booktitle={Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics},
year={2017}
}
@article{adlakha2022topiocqa,
title={TopiOCQA: Open-domain Conversational Question Answering with Topic Switching},
author={Adlakha, Vaibhav and Dhuliawala, Shehzaad and Suleman, Kaheer and de Vries, Harm and Reddy, Siva},
journal={Transactions of the Association for Computational Linguistics},
year={2022}
}
@inproceedings{nakamura2022hybridialogue,
title={HybriDialogue: An Information-Seeking Dialogue Dataset Grounded on Tabular and Textual Data},
author={Nakamura, Kai and Levy, Sharon and Tuan, Yi-Lin and Chen, Wenhu and Wang, William Yang},
booktitle={Findings of the Association for Computational Linguistics: ACL 2022},
year={2022}
}
@article{wu2023inscit,
title={InSCIt: Information-Seeking Conversations with Mixed-Initiative Interactions},
author={Wu, Zeqiu and Parish, Ryu and Cheng, Hao and Min, Sewon and Ammanabrolu, Prithviraj and Ostendorf, Mari and Hajishirzi, Hannaneh},
journal={Transactions of the Association for Computational Linguistics},
year={2023}
}