MFORT-QA: Multi-hop Few-shot Open Rich Table Question Answering

Author:

Guan Che1ORCID,Huang Mengyu2ORCID,Zhang Peng3ORCID

Affiliation:

1. AllianceBernstein, USA

2. Snowflake, USA

3. Data Science Institute/Department of Computer Science, Vanderbilt University, USA

Publisher

ACM

Reference30 articles.

1. Tom B. Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell Sandhini Agarwal Ariel Herbert-Voss Gretchen Krueger Tom Henighan Rewon Child Aditya Ramesh Daniel M. Ziegler Jeffrey Wu Clemens Winter Christopher Hesse Mark Chen Eric Sigler Mateusz Litwin Scott Gray Benjamin Chess Jack Clark Christopher Berner Sam McCandlish Alec Radford Ilya Sutskever and Dario Amodei. 2020. Language Models are Few-Shot Learners. arxiv:2005.14165 [cs.CL]

2. Wenhu Chen, Ming-Wei Chang, Eva Schlinger, William Yang Wang, and William W. Cohen. 2020. Open Question Answering over Tables and Text. CoRR abs/2010.10439 (2020). arXiv:2010.10439https://arxiv.org/abs/2010.10439

3. Wenhu Chen, Hongmin Wang, Jianshu Chen, Yunkai Zhang, Hong Wang, Shiyang Li, Xiyou Zhou, and William Yang Wang. 2019. Tabfact: A large-scale dataset for table-based fact verification. arXiv preprint arXiv:1909.02164 (2019).

4. Wenhu Chen Hanwen Zha Zhiyu Chen Wenhan Xiong Hong Wang and William Wang. 2021. HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data. arxiv:2004.07347 [cs.CL]

5. Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, 2022. Palm: Scaling language modeling with pathways. arXiv preprint arXiv:2204.02311 (2022).

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