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The rapid advancement of large language models (LLMs) has significantly impacted various domains, including healthcare and biomedicine.
Neural text generation from structured data with application to the biography domain
Rémi Lebret, David Grangier, and Michael Auli · 2016
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Sticking to the facts: Confident decoding for faithful data-to-text generation
Ran Tian, Shashi Narayan, Thibault Sellam, and Ankur P Parikh · 2019
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Modeling fluency and faithfulness for diverse neural machine translation
Yang Feng, Wanying Xie, Shuhao Gu, Chenze Shao, Wen Zhang, Zhengxin Yang, and Dong Yu · 2020
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Retrieval-augmented generation for knowledge-intensive nlp tasks
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al · 2020
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On faithfulness and factuality in abstractive summarization
Joshua Maynez, Shashi Narayan, Bernd Bohnet, and Ryan McDonald · 2020
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The ethics of ai in health care: A mapping review
Jessica Morley, Caio C.V. Machado, Christopher Burr, Josh Cowls, Indra Joshi, Mariarosaria Taddeo, and Luciano Floridi · 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
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Focus attention: Promoting faithfulness and diversity in summarization
Rahul Aralikatte, Shashi Narayan, Joshua Maynez, Sascha Rothe, and Ryan McDonald · 2021
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Neural path hunter: Reducing hallucination in dialogue systems via path grounding
Nouha Dziri, Andrea Madotto, Osmar Zaïane, and Avishek Joey Bose · 2021
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Entity-based knowledge conflicts in question answering
Shayne Longpre, Kartik Perisetla, Anthony Chen, Nikhil Ramesh, Chris DuBois, and Sameer Singh · 2021
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Generation-augmented retrieval for open-domain question answering
Yuning Mao, Pengcheng He, Xiaodong Liu, Yelong Shen, Jianfeng Gao, Jiawei Han, and Weizhu Chen · 2021
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Deduplicating training data makes language models better
Katherine Lee, Daphne Ippolito, Andrew Nystrom, Chiyuan Zhang, Douglas Eck, Chris Callison-Burch, and Nicholas Carlini · 2022
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Prompting gpt-3 to be reliable
Chenglei Si, Zhe Gan, Zhengyuan Yang, Shuohang Wang, Jianfeng Wang, Jordan Boyd-Graber, and Lijuan Wang · 2022
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Creating trustworthy llms: Dealing with hallucinations in healthcare ai
Muhammad Aurangzeb Ahmad, Ilker Yaramis, and Taposh Dutta Roy · 2023
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Hejing Cao, Zhenwei An, Jiazhan Feng, Kun Xu, Liwei Chen, and Dongyan Zhao · 2023
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UPRISE: Universal prompt retrieval for improving zero-shot evaluation
Daixuan Cheng, Shaohan Huang, Junyu Bi, Yuefeng Zhan, Jianfeng Liu, Yujing Wang, Hao Sun, Furu Wei, Weiwei Deng, and Qi Zhang · 2023
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Examining the potential of chatgpt on biomedical information retrieval: Fact-checking drug-disease associations
Zhenxiang Gao, Lingyao Li, Siyuan Ma, Qinyong Wang, Libby Hemphill, and Rong Xu · 2023
Cited alongside, same era.
Survey of hallucination in natural language generation
Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung · 2023
Cited alongside, same era.
Towards mitigating LLM hallucination via self reflection
Ziwei Ji, Tiezheng Yu, Yan Xu, Nayeon Lee, Etsuko Ishii, and Pascale Fung · 2023
Cited alongside, same era.
Haoqiang Kang, Juntong Ni, and Huaxiu Yao · 2023
Cited alongside, same era.
One llm is not enough: Harnessing the power of ensemble learning for medical question answering
Han Yang, Mingchen Li, Huixue Zhou, Yongkang Xiao, Qian Fang, and Rui Zhang · 2023
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Large language model unlearning
Yuanshun Yao, Xiaojun Xu, and Yang Liu · 2023
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R-tuning: Teaching large language models to refuse unknown questions
Hanning Zhang, Shizhe Diao, Yong Lin, Yi R Fung, Qing Lian, Xingyao Wang, Yangyi Chen, Heng Ji, and Tong Zhang · 2023
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How language model hallucinations can snowball
Muru Zhang, Ofir Press, William Merrill, Alisa Liu, and Noah A Smith · 2023
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Siren’s song in the ai ocean: a survey on hallucination in large language models
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Chain of natural language inference for reducing large language model ungrounded hallucinations
Deren Lei, Yaxi Li, Mingyu Wang, Vincent Yun, Emily Ching, Eslam Kamal, et al · 2023
Cited alongside, same era.
Med-halt: Medical domain hallucination test for large language models
Ankit Pal, Logesh Kumar Umapathi, and Malaikannan Sankarasubbu · 2023
Cited alongside, same era.
Baolin Peng, Michel Galley, Pengcheng He, Hao Cheng, Yujia Xie, Yu Hu, Qiuyuan Huang, Lars Liden, Zhou Yu, Weizhu Chen, et al · 2023
Cited alongside, same era.
Llmmaps–a visual metaphor for stratified evaluation of large language models
Patrik Puchert, Poonam Poonam, Christian van Onzenoodt, and Timo Ropinski · 2023
Cited alongside, same era.
Popular large language model chatbots’ accuracy, comprehensiveness, and self-awareness in answering ocular symptom queries
Krithi Pushpanathan, Zhi Wei Lim, Samantha Min Er Yew, David Ziyou Chen, Hazel Anne Hui’En Lin, Jocelyn Hui Lin Goh, Wendy Meihua Wong, Xiaofei Wang, Marcus Chun Jin Tan, Victor Teck Chang Koh, et al · 2023
Cited alongside, same era.
The troubling emergence of hallucination in large language models - an extensive definition, quantification, and prescriptive remediations
Vipula Rawte, Swagata Chakraborty, Agnibh Pathak, Anubhav Sarkar, S.M Towhidul Islam Tonmoy, Aman Chadha, Amit Sheth, and Amitava Das · 2023
Cited alongside, same era.
Fine-tuning language models for factuality
Katherine Tian, Eric Mitchell, Huaxiu Yao, Christopher D Manning, and Chelsea Finn · 2023
Cited alongside, same era.
Yue Zhang, Yafu Li, Leyang Cui, Deng Cai, Lemao Liu, Tingchen Fu, Xinting Huang, Enbo Zhao, Yu Zhang, Yulong Chen, et al · 2023
Later among the works it cites.
Biomedlm: A 2.7 b parameter language model trained on biomedical text
Elliot Bolton, Abhinav Venigalla, Michihiro Yasunaga, David Hall, Betty Xiong, Tony Lee, Roxana Daneshjou, Jonathan Frankle, Percy Liang, Michael Carbin, et al · 2024
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Rag and rau: A survey on retrieval-augmented language model in natural language processing
Yucheng Hu and Yuxing Lu · 2024
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When to retrieve: Teaching llms to utilize information retrieval effectively
Tiziano Labruna, Jon Ander Campos, and Gorka Azkune · 2024
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Inference-time intervention: Eliciting truthful answers from a language model
Kenneth Li, Oam Patel, Fernanda Viégas, Hanspeter Pfister, and Martin Wattenberg · 2024
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Mitigating hallucinations in large language models via self-refinement-enhanced knowledge retrieval
Mengjia Niu, Hao Li, Jie Shi, Hamed Haddadi, and Fan Mo · 2024
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Trustllm: Trustworthiness in large language models
Lichao Sun, Yue Huang, Haoran Wang, Siyuan Wu, Qihui Zhang, Chujie Gao, Yixin Huang, Wenhan Lyu, Yixuan Zhang, Xiner Li, et al · 2024
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A comprehensive survey of hallucination mitigation techniques in large language models
SM Tonmoy, SM Zaman, Vinija Jain, Anku Rani, Vipula Rawte, Aman Chadha, and Amitava Das · 2024
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How faithful are rag models? quantifying the tug-of-war between rag and llms’ internal prior
Kevin Wu, Eric Wu, and James Zou · 2024
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Meta-rewarding language models: Self-improving alignment with llm-as-a-meta-judge
Tianhao Wu, Weizhe Yuan, Olga Golovneva, Jing Xu, Yuandong Tian, Jiantao Jiao, Jason Weston, and Sainbayar Sukhbaatar · 2024
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Bmretriever: Tuning large language models as better biomedical text retrievers
Ran Xu, Wenqi Shi, Yue Yu, Yuchen Zhuang, Yanqiao Zhu, May D Wang, Joyce C Ho, Chao Zhang, and Carl Yang · 2024
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