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Augmenting Large Language Models (LLMs) for Question Answering (QA) with domain specific data has attracted wide attention.
Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton. 2008 · 2008
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al. 2018 · 2018
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019 · 2019
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Variational template machine for data-to-text generation
Rong Ye, Wenxian Shi, Hao Zhou, Zhongyu Wei, and Lei Li. 2019 · 2019
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Hybridqa: A dataset of multi-hop question answering over tabular and textual data
Wenhu Chen, Hanwen Zha, Zhiyu Chen, Wenhan Xiong, Hong Wang, and William Yang Wang. 2020c · 2020
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Don’t Stop Pretraining: Adapt Language Models to Domains and Tasks
Suchin Gururangan, Ana Marasović, Swabha Swayamdipta, Kyle Lo, Iz Beltagy, Doug Downey, and Noah A. Smith. 2020 · 2020
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Dense Passage Retrieval for Open-Domain Question Answering
Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 2020 · 2020
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Totto: A controlled table-to-text generation dataset
Ankur Parikh, Xuezhi Wang, Sebastian Gehrmann, Manaal Faruqui, Bhuwan Dhingra, Diyi Yang, and Dipanjan Das. 2020 · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020 · 2020
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Zero: Memory optimizations toward training trillion parameter models
Samyam Rajbhandari, Jeff Rasley, Olatunji Ruwase, and Yuxiong He. 2020 · 2020
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Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters
Jeff Rasley, Samyam Rajbhandari, Olatunji Ruwase, and Yuxiong He. 2020 · 2020
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Billion-scale similarity search with gpus
Jeff Johnson, Matthijs Douze, and Hervé Jégou. 2021 · 2021
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Hurdles to Progress in Long-form Question Answering
Kalpesh Krishna, Aurko Roy, and Mohit Iyyer. 2021 · 2021
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Dual reader-parser on hybrid textual and tabular evidence for open domain question answering
Alexander Hanbo Li, Patrick Ng, Peng Xu, Henghui Zhu, Zhiguo Wang, and Bing Xiang. 2021 · 2021
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Tat-qa: A question answering benchmark on a hybrid of tabular and textual content in finance
Fengbin Zhu, Wenqiang Lei, Youcheng Huang, Chao Wang, Shuo Zhang, Jiancheng Lv, Fuli Feng, and Tat-Seng Chua. 2021 · 2021
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Langchain
Harrison Chase. 2022 · 2022
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HiTab: A hierarchical table dataset for question answering and natural language generation
Zhoujun Cheng, Haoyu Dong, Zhiruo Wang, Ran Jia, Jiaqi Guo, Yan Gao, Shi Han, Jian-Guang Lou, and Dongmei Zhang. 2022 · 2022
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Mixed-modality Representation Learning and Pre-training for Joint Table-and-Text Retrieval in OpenQA
Junjie Huang, Wanjun Zhong, Qian Liu, Ming Gong, Daxin Jiang, and Nan Duan. 2022 · 2022
Cited alongside, same era.
PLOG: Table-to-logic pretraining for logical table-to-text generation
Ao Liu, Haoyu Dong, Naoaki Okazaki, Shi Han, and Dongmei Zhang. 2022 · 2022
Cited alongside, same era.
Few-shot Table-to-text Generation with Prefix-Controlled Generator
Yutao Luo, Menghua Lu, Gongshen Liu, and Shilin Wang. 2022 · 2022
Cited alongside, same era.
FeTaQA: Free-form Table Question Answering
Linyong Nan, Chiachun Hsieh, Ziming Mao, Xi Victoria Lin, Neha Verma, Rui Zhang, Wojciech Kryściński, Hailey Schoelkopf, Riley Kong, Xiangru Tang, Mutethia Mutuma, Ben Rosand, Isabel Trindade, Renusree Bandaru, Jacob Cunningham, Caiming Xiong, Dragomir Radev, and Dragomir Radev. 2022 · 2022
Cited alongside, same era.
Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022 · 2022
Cited alongside, same era.
Table-gpt: Table-tuned gpt for diverse table tasks
Peng Li, Yeye He, Dror Yashar, Weiwei Cui, Song Ge, Haidong Zhang, Danielle Rifinski Fainman, Dongmei Zhang, and Surajit Chaudhuri. 2023 · 2023
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Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey
Chen Ling, Xujiang Zhao, Jiaying Lu, Chengyuan Deng, Can Zheng, Junxiang Wang, Tanmoy Chowdhury, Yun Li, Hejie Cui, Xuchao Zhang, Tianjiao Zhao, Amit Panalkar, Wei Cheng, Haoyu Wang, Yanchi Liu, Zhengzhang Chen, Haifeng Chen, Chris White, Quanquan Gu, Jian Pei, and Liang Zhao. 2023 · 2023
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G-eval: NLG evaluation using gpt-4 with better human alignment
Yang Liu, Dan Iter, Yichong Xu, Shuohang Wang, Ruochen Xu, and Chenguang Zhu. 2023 · 2023
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BioMedGPT: Open Multimodal Generative Pre-trained Transformer for BioMedicine
Yizhen Luo, Jiahuan Zhang, Siqi Fan, Kai Yang, Yushuai Wu, Mu Qiao, and Zaiqing Nie. 2023 · 2023
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Impact of pretraining term frequencies on few-shot numerical reasoning
Yasaman Razeghi, Robert L Logan IV, Matt Gardner, and Sameer Singh. 2022 · 2022
Cited alongside, same era.
UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models
Tianbao Xie, Chen Henry Wu, Peng Shi, Ruiqi Zhong, Torsten Scholak, Michihiro Yasunaga, Chien-Sheng Wu, Ming Zhong, Pengcheng Yin, Sida I. Wang, Victor Zhong, Bailin Wang, Chengzu Li, Connor Boyle, Ansong Ni, Ziyu Yao, Dragomir Radev, Caiming Xiong, Lingpeng Kong, Rui Zhang, Noah A. Smith, Luke Zettlemoyer, and Tao Yu. 2022 · 2022
Cited alongside, same era.
OPT: Open Pre-trained Transformer Language Models
Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, Todor Mihaylov, Myle Ott, Sam Shleifer, Kurt Shuster, Daniel Simig, Punit Singh Koura, Anjali Sridhar, Tianlu Wang, and Luke Zettlemoyer. 2022 · 2022
Cited alongside, same era.
Reasoning over hybrid chain for table-and-text open domain question answering
Wanjun Zhong, Junjie Huang, Qian Liu, Ming Zhou, Jiahai Wang, Jian Yin, and Nan Duan. 2022 · 2022
Cited alongside, same era.
Hellama: Llama-based table to text generation by highlighting the important evidence
Junyi Bian, Xiaolei Qin, Wuhe Zou, Mengzuo Huang, and Weidong Zhang. 2023 · 2023
Cited alongside, same era.
Pythia: A suite for analyzing large language models across training and scaling
Stella Biderman, Hailey Schoelkopf, Quentin Gregory Anthony, Herbie Bradley, Kyle O’Brien, Eric Hallahan, Mohammad Aflah Khan, Shivanshu Purohit, Usvsn Sai Prashanth, Edward Raff, Aviya Skowron, Lintang Sutawika, and Oskar Van Der Wal. 2023 · 2023
Cited alongside, same era.
QLoRA: Efficient Finetuning of Quantized LLMs
Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer. 2023 · 2023
Cited alongside, same era.
Query rewriting in retrieval-augmented large language models
Xinbei Ma, Yeyun Gong, Pengcheng He, Hai Zhao, and Nan Duan. 2023 · 2023
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Yuan Sui, Mengyu Zhou, Mingjie Zhou, Shi Han, and Dongmei Zhang. 2023 · 2023
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MVP: Multi-task Supervised Pre-training for Natural Language Generation
Tianyi Tang, Junyi Li, Wayne Xin Zhao, and Ji-Rong Wen. 2023 · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. 2023 · 2023
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PMC-LLaMA: Towards Building Open-source Language Models for Medicine
Chaoyi Wu, Weixiong Lin, Xiaoman Zhang, Ya Zhang, Yanfeng Wang, and Weidi Xie. 2023 · 2023
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Retrieval-augmented domain adaptation of language models
Benfeng Xu, Chunxu Zhao, Wenbin Jiang, PengFei Zhu, Songtai Dai, Chao Pang, Zhuo Sun, Shuohuan Wang, and Yu Sun. 2023 · 2023
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Empower large language model to perform better on industrial domain-specific question answering
Fangkai Yang, Pu Zhao, Zezhong Wang, Lu Wang, Bo Qiao, Jue Zhang, Mohit Garg, Qingwei Lin, Saravan Rajmohan, and Dongmei Zhang. 2023 · 2023
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Frequency Balanced Datasets Lead to Better Language Models
Rodolfo Zevallos, Mireia Farrús, and Núria Bel. 2023 · 2023
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Retrieve Anything To Augment Large Language Models
Peitian Zhang, Shitao Xiao, Zheng Liu, Zhicheng Dou, and Jian-Yun Nie. 2023 · 2023
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Investigating table-to-text generation capabilities of large language models in real-world information seeking scenarios
Yilun Zhao, Haowei Zhang, Shengyun Si, Linyong Nan, Xiangru Tang, and Arman Cohan. 2023b · 2023
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Rag vs fine-tuning: Pipelines, tradeoffs, and a case study on agriculture
Aman Gupta, Anup Shirgaonkar, Angels de Luis Balaguer, Bruno Silva, Daniel Holstein, Dawei Li, Jennifer Marsman, Leonardo O Nunes, Mahsa Rouzbahman, Morris Sharp, et al. 2024 · 2024
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Fine tuning vs. retrieval augmented generation for less popular knowledge
Heydar Soudani, Evangelos Kanoulas, and Faegheh Hasibi. 2024 · 2024
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