Update README.md

README.md

@@ -14,42 +14,116 @@ configs:
 - config_name: coqa
   data_files:
     - split: dev
-      path: coqa/*
+      path: data/coqa/*
 - config_name: inscit
   data_files:
     - split: dev
-      path: inscit/*
+      path: data/inscit/*
 - config_name: inscit
   data_files:
     - split: dev
-      path: inscit/*
+      path: data/inscit/*
 - config_name: topiocqa
   data_files:
     - split: dev
-      path: topiocqa/*
+      path: data/topiocqa/*
 - config_name: hybridial
   data_files:
     - split: test
-      path: hybridial/*
+      path: data/hybridial/*
 - config_name: doc2dial
   data_files:
     - split: test
-      path: doc2dial/test.json
+      path: data/doc2dial/test.json
 - config_name: quac
   data_files:
     - split: test
-      path: quac/test.json
+      path: data/quac/test.json
 - config_name: qrecc
   data_files:
     - split: test
-      path: qrecc/test.json
+      path: data/qrecc/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, and conduct arithmetic calculations.
+
+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.
+
+## Evaluation
+We open-source the scripts for running and evaluating on ConvRAG
+
+
 ## License
 The ConvRAG are built on and derived from existing datasets. We refer users to the original licenses accompanying each dataset.
 
 
 ## Citation
+If you evaluate using ConvRAG, please cite all the datasets you use.
 <pre>
-
+@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}
+}
+@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}}
 </pre>