Fetching the paper…
Reading the bibliography…
Retrieval-Augmented Generation (RAG) effectively reduces hallucinations in Large Language Models (LLMs) but can still produce inconsistent or unsupported content.
Pubmedqa: A dataset for biomedical research question answering
Qiao Jin, Bhuwan Dhingra, Zhengping Liu, William W. Cohen, and Xinghua Lu. 2019 · 1909
Earlier work this paper cites.
Dataset and neural recurrent sequence labeling model for open-domain factoid question answering
Peng Li, Wei Li, Zhengyan He, Xuguang Wang, Ying Cao, Jie Zhou, and Wei Xu. 2016 · 2016
Earlier work this paper cites.
Creating training corpora for NLG micro-planners
Claire Gardent, Anastasia Shimorina, Shashi Narayan, and Laura Perez-Beltrachini. 2017 · 2017
Earlier work this paper cites.
DROP: A reading comprehension benchmark requiring discrete reasoning over paragraphs
Dheeru Dua, Yizhong Wang, Pradeep Dasigi, Gabriel Stanovsky, Sameer Singh, and Matt Gardner. 2019 · 2019
Earlier work this paper cites.
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, Sebastian Riedel, and Douwe Kiela. 2020 · 2020
Earlier work this paper cites.
COVID-QA: A question answering dataset for COVID-19
Timo Möller, Anthony Reina, Raghavan Jayakumar, and Malte Pietsch. 2020 · 2020
Earlier work this paper cites.
Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters
Jeff Rasley, Samyam Rajbhandari, Olatunji Ruwase, and Yuxiong He. 2020 · 2020
Earlier work this paper cites.
Findings of the 2021 conference on machine translation (WMT21)
Farhad Akhbardeh, Arkady Arkhangorodsky, Magdalena Biesialska, Ondřej Bojar, Rajen Chatterjee, Vishrav Chaudhary, Marta R. Costa-jussa, Cristina España-Bonet, Angela Fan, Christian Federmann, Markus Freitag, Yvette Graham, Roman Grundkiewicz, Barry Haddow, Leonie Harter, Kenneth Heafield, Christopher Homan, Matthias Huck, Kwabena Amponsah-Kaakyire, Jungo Kasai, Daniel Khashabi, Kevin Knight, Tom Kocmi, Philipp Koehn, Nicholas Lourie, Christof Monz, Makoto Morishita, Masaaki Nagata, Ajay Nagesh, Toshiaki Nakazawa, Matteo Negri, Santanu Pal, Allahsera Auguste Tapo, Marco Turchi, Valentin Vydrin, and Marcos Zampieri. 2021 · 2021
Earlier work this paper cites.
Improving language models by retrieving from trillions of tokens
Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai, Eliza Rutherford, Katie Millican, George van den Driessche, Jean-Baptiste Lespiau, Bogdan Damoc, Aidan Clark, Diego de Las Casas, Aurelia Guy, Jacob Menick, Roman Ring, T. W. Hennigan, Saffron Huang, Lorenzo Maggiore, Chris Jones, Albin Cassirer, Andy Brock, Michela Paganini, Geoffrey Irving, Oriol Vinyals, Simon Osindero, Karen Simonyan, Jack W. Rae, Erich Elsen, and L. Sifre. 2021 · 2021
Earlier work this paper cites.
CSDS: A fine-grained Chinese dataset for customer service dialogue summarization
Haitao Lin, Liqun Ma, Junnan Zhu, Lu Xiang, Yu Zhou, Jiajun Zhang, and Chengqing Zong. 2021 · 2021
Earlier work this paper cites.
Entity-based knowledge conflicts in question answering
Shayne Longpre, Kartik Perisetla, Anthony Chen, Nikhil Ramesh, Chris DuBois, and Sameer Singh. 2021 · 2021
Earlier work this paper cites.
On the origin of hallucinations in conversational models: Is it the datasets or the models?
Nouha Dziri, Sivan Milton, Mo Yu, Osmar Zaiane, and Siva Reddy. 2022 · 2022
Earlier work this paper cites.
LoRA: Low-rank adaptation of large language models
Edward J Hu, yelong shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2022 · 2022
Earlier work this paper cites.
Chatgpt: Optimizing language models for dialogue
OpenAI. 2022 · 2022
Earlier work this paper cites.
ASQA: Factoid questions meet long-form answers
Ivan Stelmakh, Yi Luan, Bhuwan Dhingra, and Ming-Wei Chang. 2022 · 2022
Cited alongside, same era.
Rethinking document-level neural machine translation
Zewei Sun, Mingxuan Wang, Hao Zhou, Chengqi Zhao, Shujian Huang, Jiajun Chen, and Lei Li. 2022 · 2022
Cited alongside, same era.
Financebench: A new benchmark for financial question answering
Pranab Islam, Anand Kannappan, Douwe Kiela, Rebecca Qian, Nino Scherrer, and Bertie Vidgen. 2023 · 2023
Cited alongside, same era.
Efficient memory management for large language model serving with pagedattention
Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph E. Gonzalez, Hao Zhang, and Ion Stoica. 2023 · 2023
Cited alongside, same era.
HaluEval: A large-scale hallucination evaluation benchmark for large language models
Junyi Li, Xiaoxue Cheng, Xin Zhao, Jian-Yun Nie, and Ji-Rong Wen. 2023 · 2023
Cited alongside, same era.
LawBench: Benchmarking legal knowledge of large language models
Zhiwei Fei, Xiaoyu Shen, Dawei Zhu, Fengzhe Zhou, Zhuo Han, Alan Huang, Songyang Zhang, Kai Chen, Zhixin Yin, Zongwen Shen, Jidong Ge, and Vincent Ng. 2024 · 2024
Later among the works it cites.
Efficient knowledge infusion via KG-LLM alignment
Zhouyu Jiang, Ling Zhong, Mengshu Sun, Jun Xu, Rui Sun, Hui Cai, Shuhan Luo, and Zhiqiang Zhang. 2024 · 2024
Later among the works it cites.
The dawn after the dark: An empirical study on factuality hallucination in large language models
Junyi Li, Jie Chen, Ruiyang Ren, Xiaoxue Cheng, Xin Zhao, Jian-Yun Nie, and Ji-Rong Wen. 2024 · 2024
Later among the works it cites.
RAGTruth: A hallucination corpus for developing trustworthy retrieval-augmented language models
Cheng Niu, Yuanhao Wu, Juno Zhu, Siliang Xu, KaShun Shum, Randy Zhong, Juntong Song, and Tong Zhang. 2024 · 2024
Later among the works it cites.
Qwen2.5: A party of foundation models
Team Qwen. 2024 · 2024
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
When not to trust language models: Investigating effectiveness of parametric and non-parametric memories
Alex Mallen, Akari Asai, Victor Zhong, Rajarshi Das, Daniel Khashabi, and Hannaneh Hajishirzi. 2023 · 2023
Cited alongside, same era.
WebCPM: Interactive web search for Chinese long-form question answering
Yujia Qin, Zihan Cai, Dian Jin, Lan Yan, Shihao Liang, Kunlun Zhu, Yankai Lin, Xu Han, Ning Ding, Huadong Wang, Ruobing Xie, Fanchao Qi, Zhiyuan Liu, Maosong Sun, and Jie Zhou. 2023 · 2023
Cited alongside, same era.
Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn. 2023 · 2023
Cited alongside, same era.
Evaluating the factual consistency of large language models through news summarization
Derek Tam, Anisha Mascarenhas, Shiyue Zhang, Sarah Kwan, Mohit Bansal, and Colin Raffel. 2023 · 2023
Cited alongside, same era.
Neeraj Varshney, Wenlin Yao, Hongming Zhang, Jianshu Chen, and Dong Yu. 2023 · 2023
Cited alongside, same era.
IfQA: A dataset for open-domain question answering under counterfactual presuppositions
Wenhao Yu, Meng Jiang, Peter Clark, and Ashish Sabharwal. 2023 · 2023
Cited alongside, same era.
Llama 3 model card
AI@Meta. 2024 · 2024
Cited alongside, same era.
Selvan Sunitha Ravi, Bartosz Mielczarek, Anand Kannappan, Douwe Kiela, and Rebecca Qian. 2024 · 2024
Later among the works it cites.
ARES: An automated evaluation framework for retrieval-augmented generation systems
Jon Saad-Falcon, Omar Khattab, Christopher Potts, and Matei Zaharia. 2024 · 2024
Later among the works it cites.
Tablebench: A comprehensive and complex benchmark for table question answering
Xianjie Wu, Jian Yang, Linzheng Chai, Ge Zhang, Jiaheng Liu, Xinrun Du, Di Liang, Daixin Shu, Xianfu Cheng, Tianzhen Sun, Guanglin Niu, Tongliang Li, and Zhoujun Li. 2024 · 2024
Later among the works it cites.
Adaptive chameleon or stubborn sloth: Revealing the behavior of large language models in knowledge conflicts
Jian Xie, Kai Zhang, Jiangjie Chen, Renze Lou, and Yu Su. 2024 · 2024
Later among the works it cites.
Trustworthy alignment of retrieval-augmented large language models via reinforcement learning
Zongmeng Zhang, Yufeng Shi, Jinhua Zhu, Wengang Zhou, Xiang Qi, Peng Zhang, and Houqiang Li. 2024 · 2024
Later among the works it cites.
LlamaFactory: Unified efficient fine-tuning of 100+ language models
Yaowei Zheng, Richong Zhang, Junhao Zhang, Yanhan Ye, and Zheyan Luo. 2024 · 2024
Later among the works it cites.
A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions
Lei Huang, Weijiang Yu, Weitao Ma, Weihong Zhong, Zhangyin Feng, Haotian Wang, Qianglong Chen, Weihua Peng, Xiaocheng Feng, Bing Qin, and Ting Liu. 2025 · 2025
Closest in time.
Crud-rag: A comprehensive chinese benchmark for retrieval-augmented generation of large language models
Yuanjie Lyu, Zhiyu Li, Simin Niu, Feiyu Xiong, Bo Tang, Wenjin Wang, Hao Wu, Huanyong Liu, Tong Xu, and Enhong Chen. 2025 · 2025
Closest in time.