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Data annotation and synthesis generally refers to the labeling or generating of raw data with relevant information, which could be used for improving the efficacy of machine learning models.
The turking test: Can language models understand instructions?
Avia Efrat and Omer Levy. 2020 · 2010
Earlier work this paper cites.
A large annotated corpus for learning natural language inference
Samuel R Bowman, Gabor Angeli, Christopher Potts, and Christopher D Manning. 2015 · 2015
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Squad: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
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Newsqa: A machine comprehension dataset
Adam Trischler, Tong Wang, Xingdi Yuan, Justin Harris, Alessandro Sordoni, Philip Bachman, and Kaheer Suleman. 2016 · 2016
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spaCy 2: Natural language understanding with Bloom embeddings, convolutional neural networks and incremental parsing
Matthew Honnibal and Ines Montani. 2017 · 2017
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Dailydialog: A manually labelled multi-turn dialogue dataset
Yanran Li, Hui Su, Xiaoyu Shen, Wenjie Li, Ziqiang Cao, and Shuzi Niu. 2017 · 2017
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Program induction by rationale generation: Learning to solve and explain algebraic word problems
Wang Ling, Dani Yogatama, Chris Dyer, and Phil Blunsom. 2017 · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
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Prodigy: A new annotation tool for radically efficient machine teaching
Ines Montani and Matthew Honnibal. 2018 · 2018
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Glue: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R Bowman. 2018 · 2018
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Semantic role labeling with pretrained language models for known and unknown predicates
Daniil Larionov, Artem Shelmanov, Elena Chistova, and Ivan Smirnov. 2019 · 2019
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Huggingface’s transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush. 2020 · 2020
Earlier work this paper cites.
Persistent anti-muslim bias in large language models
Abubakar Abid, Maheen Farooqi, and James Zou. 2021 · 2021
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Ubiai: Text annotation tool
Walid Amamou. 2021 · 2021
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Efficient large scale language modeling with mixtures of experts
Mikel Artetxe, Shruti Bhosale, Naman Goyal, Todor Mihaylov, Myle Ott, Sam Shleifer, Xi Victoria Lin, Jingfei Du, Srinivasan Iyer, Ramakanth Pasunuru, et al. 2021 · 2021
Earlier work this paper cites.
Socially responsible ai algorithms: Issues, purposes, and challenges
Lu Cheng, Kush R Varshney, and Huan Liu. 2021 · 2021
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Training verifiers to solve math word problems
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al. 2021 · 2021
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Making pre-trained language models better few-shot learners
Tianyu Gao, Adam Fisch, and Danqi Chen. 2021 · 2021
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Recent advances in natural language processing via large pre-trained language models: A survey
Bonan Min, Hayley Ross, Elior Sulem, Amir Pouran Ben Veyseh, Thien Huu Nguyen, Oscar Sainz, Eneko Agirre, Ilana Heintz, and Dan Roth. 2021 · 2021
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Gpt3mix: Leveraging large-scale language models for text augmentation
Kang Min Yoo, Dongju Park, Jaewook Kang, Sang-Woo Lee, and Woomyoung Park. 2021 · 2021
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Lmturk: Few-shot learners as crowdsourcing workers in a language-model-as-a-service framework
Mengjie Zhao, Fei Mi, Yasheng Wang, Minglei Li, Xin Jiang, Qun Liu, and Hinrich Schütze. 2021 · 2021
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Stack ai: The middle-layer of ai
Bernardo Aceituno and Antoni Rosinol. 2022 · 2022
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Large language models are zero-shot clinical information extractors
Monica Agrawal, Stefan Hegselmann, Hunter Lang, Yoon Kim, and David Sontag. 2022 · 2022
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Constitutional ai: Harmlessness from ai feedback
Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, et al. 2022 · 2022
Earlier work this paper cites.
Weakly supervised data augmentation through prompting for dialogue understanding
Maximillian Chen, Alexandros Papangelis, Chenyang Tao, Andy Rosenbaum, Seokhwan Kim, Yang Liu, Zhou Yu, and Dilek Hakkani-Tur. 2022 · 2022
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Langchain
Chase Harrison. 2022 · 2022
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Unnatural instructions: Tuning language models with (almost) no human labor
Or Honovich, Thomas Scialom, Omer Levy, and Timo Schick. 2022 · 2022
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Language models as zero-shot planners: Extracting actionable knowledge for embodied agents
Wenlong Huang, Pieter Abbeel, Deepak Pathak, and Igor Mordatch. 2022 · 2022
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Hyuhng Joon Kim, Hyunsoo Cho, Junyeob Kim, Taeuk Kim, Kang Min Yoo, and Sang-goo Lee. 2022 · 2022
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Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022 · 2022
Earlier work this paper cites.
Reward design with language models
Minae Kwon, Sang Michael Xie, Kalesha Bullard, and Dorsa Sadigh. 2022 · 2022
Earlier work this paper cites.
Teaching models to express their uncertainty in words
Stephanie Lin, Jacob Hilton, and Owain Evans. 2022 · 2022
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Generating training data with language models: Towards zero-shot language understanding
Yu Meng, Jiaxin Huang, Yu Zhang, and Jiawei Han. 2022 · 2022
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Codegen: An open large language model for code with multi-turn program synthesis
Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong. 2022 · 2022
Earlier work this paper cites.
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
Earlier work this paper cites.
Moving from tabular knowledge graph quality assessment to rdf triples leveraging chatgpt
Gabriele Tuozzo. 2022 · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022 · 2022
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Asdot: Any-shot data-to-text generation with pretrained language models
Jiannan Xiang, Zhengzhong Liu, Yucheng Zhou, Eric Xing, and Zhiting Hu. 2022 · 2022
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Zerogen: Efficient zero-shot learning via dataset generation
Jiacheng Ye, Jiahui Gao, Qintong Li, Hang Xu, Jiangtao Feng, Zhiyong Wu, Tao Yu, and Lingpeng Kong. 2022a · 2022
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Progen: Progressive zero-shot dataset generation via in-context feedback
Jiacheng Ye, Jiahui Gao, Zhiyong Wu, Jiangtao Feng, Tao Yu, and Lingpeng Kong. 2022b · 2022
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Generate rather than retrieve: Large language models are strong context generators
Wenhao Yu, Dan Iter, Shuohang Wang, Yichong Xu, Mingxuan Ju, Soumya Sanyal, Chenguang Zhu, Michael Zeng, and Meng Jiang. 2022 · 2022
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Reflect, not reflex: Inference-based common ground improves dialogue response quality
Pei Zhou, Hyundong Cho, Pegah Jandaghi, Dong-Ho Lee, Bill Yuchen Lin, Jay Pujara, and Xiang Ren. 2022a · 2022
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Llm based generation of item-description for recommendation system
Arkadeep Acharya, Brijraj Singh, and Naoyuki Onoe. 2023 · 2023
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Self-consuming generative models go mad
Sina Alemohammad, Josue Casco-Rodriguez, Lorenzo Luzi, Ahmed Imtiaz Humayun, Hossein Reza Babaei, Daniel LeJeune, Ali Siahkoohi, and Richard Baraniuk. 2023 · 2023
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Meysam Alizadeh, Maël Kubli, Zeynab Samei, Shirin Dehghani, Juan Diego Bermeo, Maria Korobeynikova, and Fabrizio Gilardi. 2023 · 2023
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Artificial hallucinations in chatgpt: implications in scientific writing
Hussam Alkaissi and Samy I McFarlane. 2023 · 2023
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Learning from mistakes makes llm better reasoner
Shengnan An, Zexiong Ma, Zeqi Lin, Nanning Zheng, Jian-Guang Lou, and Weizhu Chen. 2023 · 2023
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Large language models and the perils of their hallucinations
Razvan Azamfirei, Sapna R Kudchadkar, and James Fackler. 2023 · 2023
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It’s not easy being wrong: Evaluating process of elimination reasoning in large language models
Nishant Balepur, Shramay Palta, and Rachel Rudinger. 2023 · 2023
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Closing the loop: Testing chatgpt to generate model explanations to improve human labelling of sponsored content on social media
Thales Bertaglia, Stefan Huber, Catalina Goanta, Gerasimos Spanakis, and Adriana Iamnitchi. 2023 · 2023
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A drop of ink may make a million think: The spread of false information in large language models
Ning Bian, Peilin Liu, Xianpei Han, Hongyu Lin, Yaojie Lu, Ben He, and Le Sun. 2023 · 2023
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Do as i can, not as i say: Grounding language in robotic affordances
Anthony Brohan, Yevgen Chebotar, Chelsea Finn, Karol Hausman, Alexander Herzog, Daniel Ho, Julian Ibarz, Alex Irpan, Eric Jang, Ryan Julian, et al. 2023 · 2023
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A survey on evaluation of large language models
Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, and Xing Xie. 2023 · 2023
Earlier work this paper cites.
Code alpaca: An instruction-following llama model for code generation
Sahil Chaudhary. 2023 · 2023
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Can llm-generated misinformation be detected?
Canyu Chen and Kai Shu. 2023 · 2023
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Places: Prompting language models for social conversation synthesis
Maximillian Chen, Alexandros Papangelis, Chenyang Tao, Seokhwan Kim, Andy Rosenbaum, Yang Liu, Zhou Yu, and Dilek Hakkani-Tur. 2023b · 2023
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Self-icl: Zero-shot in-context learning with self-generated demonstrations
Wei-Lin Chen, Cheng-Kuang Wu, Yun-Nung Chen, and Hsin-Hsi Chen. 2023d · 2023
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Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality
Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E Gonzalez, et al. 2023b · 2023
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Lm vs lm: Detecting factual errors via cross examination
Roi Cohen, May Hamri, Mor Geva, and Amir Globerson. 2023 · 2023
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Chatlaw: Open-source legal large language model with integrated external knowledge bases
Jiaxi Cui, Zongjian Li, Yang Yan, Bohua Chen, and Li Yuan. 2023 · 2023
Earlier work this paper cites.
Rephrase and respond: Let large language models ask better questions for themselves
Yihe Deng, Weitong Zhang, Zixiang Chen, and Quanquan Gu. 2023 · 2023
Cited alongside, same era.
Can ai language models replace human participants?
Danica Dillion, Niket Tandon, Yuling Gu, and Kurt Gray. 2023 · 2023
Cited alongside, same era.
Raft: Reward ranked finetuning for generative foundation model alignment
Hanze Dong, Wei Xiong, Deepanshu Goyal, Rui Pan, Shizhe Diao, Jipeng Zhang, Kashun Shum, and Tong Zhang. 2023 · 2023
Cited alongside, same era.
Gpts are gpts: An early look at the labor market impact potential of large language models
Tyna Eloundou, Sam Manning, Pamela Mishkin, and Daniel Rock. 2023 · 2023
Cited alongside, same era.
Annotated dataset creation through large language models for non-english medical nlp
A new benchmark and reverse validation method for passage-level hallucination detection
Shiping Yang, Renliang Sun, and Xiaojun Wan. 2023d · 2023
Later among the works it cites.
Beyond chain-of-thought, effective graph-of-thought reasoning in large language models
Yao Yao, Zuchao Li, and Hai Zhao. 2023 · 2023
Later among the works it cites.
Exchange-of-thought: Enhancing large language model capabilities through cross-model communication
Zhangyue Yin, Qiushi Sun, Cheng Chang, Qipeng Guo, Junqi Dai, Xuan-Jing Huang, and Xipeng Qiu. 2023 · 2023
Later among the works it cites.
Temporal data meets llm–explainable financial time series forecasting
Xinli Yu, Zheng Chen, Yuan Ling, Shujing Dong, Zongyi Liu, and Yanbin Lu. 2023 · 2023
Later among the works it cites.
Large language models meet nl2code: A survey
Daoguang Zan, Bei Chen, Fengji Zhang, Dianjie Lu, Bingchao Wu, Bei Guan, Wang Yongji, and Jian-Guang Lou. 2023 · 2023
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Johann Frei and Frank Kramer. 2023 · 2023
Cited alongside, same era.
Improving language model negotiation with self-play and in-context learning from ai feedback
Yao Fu, Hao Peng, Tushar Khot, and Mirella Lapata. 2023 · 2023
Cited alongside, same era.
Human-like summarization evaluation with chatgpt
Mingqi Gao, Jie Ruan, Renliang Sun, Xunjian Yin, Shiping Yang, and Xiaojun Wan. 2023 · 2023
Cited alongside, same era.
Koala: A dialogue model for academic research
Xinyang Geng, Arnav Gudibande, Hao Liu, Eric Wallace, Pieter Abbeel, Sergey Levine, and Dawn Song. 2023 · 2023
Cited alongside, same era.
The false promise of imitating proprietary llms
Arnav Gudibande, Eric Wallace, Charles Burton Snell, Xinyang Geng, Hao Liu, P. Abbeel, Sergey Levine, and Dawn Song. 2023 · 2023
Cited alongside, same era.
Reinforced self-training (rest) for language modeling
Caglar Gulcehre, Tom Le Paine, Srivatsan Srinivasan, Ksenia Konyushkova, Lotte Weerts, Abhishek Sharma, Aditya Siddhant, Alex Ahern, Miaosen Wang, Chenjie Gu, et al. 2023 · 2023
Cited alongside, same era.
Suriya Gunasekar, Yi Zhang, Jyoti Aneja, Caio Cesar Teodoro Mendes, Allison Del Giorno, Sivakanth Gopi, Mojan Javaheripi, Piero C. Kauffmann, Gustavo de Rosa, Olli Saarikivi, Adil Salim, S. Shah, Harkirat Singh Behl, Xin Wang, Sébastien Bubeck, Ronen Eldan, Adam Tauman Kalai, Yin Tat Lee, and Yuan-Fang Li. 2023 · 2023
Cited alongside, same era.
Targen: Targeted data generation with large language models
Himanshu Gupta, Kevin Scaria, Ujjwala Anantheswaran, Shreyas Verma, Mihir Parmar, Saurabh Arjun Sawant, Swaroop Mishra, and Chitta Baral. 2023 · 2023
Cited alongside, same era.
Later among the works it cites.
Huatuogpt, towards taming language model to be a doctor
Hongbo Zhang, Junying Chen, Feng Jiang, Fei Yu, Zhihong Chen, Guiming Chen, Jianquan Li, Xiangbo Wu, Zhang Zhiyi, Qingying Xiao, et al. 2023 · 2023
Later among the works it cites.
Augesc: Dialogue augmentation with large language models for emotional support conversation
Chujie Zheng, Sahand Sabour, Jiaxin Wen, Zheng Zhang, and Minlie Huang. 2023a · 2023
Later among the works it cites.
Can llms replace manual annotation of software engineering artifacts?
Toufique Ahmed, Premkumar Devanbu, Christoph Treude, and Michael Pradel. 2024 · 2024
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Fill in the gaps: Model calibration and generalization with synthetic data
Yang Ba, Michelle Mancenido, and Rong Pan. 2024 · 2024
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Alimohammad Beigi, Zhen Tan, Nivedh Mudiam, Canyu Chen, Kai Shu, and Huan Liu. 2024 · 2024
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Graph of thoughts: Solving elaborate problems with large language models
Maciej Besta, Nils Blach, Ales Kubicek, Robert Gerstenberger, Michal Podstawski, Lukas Gianinazzi, Joanna Gajda, Tomasz Lehmann, Hubert Niewiadomski, Piotr Nyczyk, et al. 2024 · 2024
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Zero-shot llm-guided counterfactual generation for text
Amrita Bhattacharjee, Raha Moraffah, Joshua Garland, and Huan Liu. 2024 · 2024
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Codekgc: Code language model for generative knowledge graph construction
Zhen Bi, Jing Chen, Yinuo Jiang, Feiyu Xiong, Wei Guo, Huajun Chen, and Ningyu Zhang. 2024 · 2024
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Adjudicating llms as propbank annotators
Julia Bonn, Harish Tayyar Madabushi, Jena D Hwang, and Claire Bonial. 2024 · 2024
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Scaling synthetic data creation with 1,000,000,000 personas
Xin Chan, Xiaoyang Wang, Dian Yu, Haitao Mi, and Dong Yu. 2024 · 2024
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Manav Chaudhary, Harshit Gupta, and Vasudeva Varma. 2024 · 2024
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Controlmath: Controllable data generation promotes math generalist models
Nuo Chen, Ning Wu, Jianhui Chang, Linjun Shou, and Jia Li. 2024a · 2024
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From the least to the most: Building a plug-and-play visual reasoner via data synthesis
Chuanqi Cheng, Jian Guan, Wei Wu, and Rui Yan. 2024a · 2024
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Investigating annotator bias in large language models for hate speech detection
Amit Das, Zheng Zhang, Fatemeh Jamshidi, Vinija Jain, Aman Chadha, Nilanjana Raychawdhary, Mary Sandage, Lauramarie Pope, Gerry Dozier, and Cheryl Seals. 2024 · 2024
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Automated construction of theme-specific knowledge graphs
Linyi Ding, Sizhe Zhou, Jinfeng Xiao, and Jiawei Han. 2024 · 2024
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Synthesizrr: Generating diverse datasets with retrieval augmentation
Abhishek Divekar and Greg Durrett. 2024 · 2024
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Improving language model reasoning with self-motivated learning
Yunlong Feng, Yang Xu, Libo Qin, Yasheng Wang, and Wanxiang Che. 2024 · 2024
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Large language models improve annotation of prokaryotic viral proteins
Zachary N Flamholz, Steven J Biller, and Libusha Kelly. 2024 · 2024
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Matthias Gerstgrasser, Rylan Schaeffer, Apratim Dey, Rafael Rafailov, Henry Sleight, John Hughes, Tomasz Korbak, Rajashree Agrawal, Dhruv Pai, Andrey Gromov, et al. 2024 · 2024
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Chatgpt as your n-th annotator: Experiments in leveraging large language models for social science text annotation in slovak language
Endre Hamerlik, Marek Šuppa, Miroslav Blšták, Jozef Kubík, Martin Takáč, Marián Šimko, and Andrej Findor. 2024 · 2024
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Self-demos: Eliciting out-of-demonstration generalizability in large language models
Wei He, Shichun Liu, Jun Zhao, Yiwen Ding, Yi Lu, Zhiheng Xi, Tao Gui, Qi Zhang, and Xuanjing Huang. 2024 · 2024
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Disinformation detection: An evolving challenge in the age of llms
Bohan Jiang, Zhen Tan, Ayushi Nirmal, and Huan Liu. 2024a · 2024
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Knowledge-augmented reasoning distillation for small language models in knowledge-intensive tasks
Minki Kang, Seanie Lee, Jinheon Baek, Kenji Kawaguchi, and Sung Ju Hwang. 2024 · 2024
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Longform: Effective instruction tuning with reverse instructions
Abdullatif Köksal, Timo Schick, Anna Korhonen, and Hinrich Schuetze · 2024
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Corrsynth-a correlated sampling method for diverse dataset generation from llms
Suhas Kowshik, Abhishek Divekar, and Vijit Malik. 2024 · 2024
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Zero-shot cross-lingual transfer for synthetic data generation in grammatical error detection
Gaetan Lopez Latouche, Marc-André Carbonneau, and Ben Swanson. 2024 · 2024
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Source2synth: Synthetic data generation and curation grounded in real data sources
Alisia Lupidi, Carlos Gemmell, Nicola Cancedda, Jane Dwivedi-Yu, Jason Weston, Jakob Foerster, Roberta Raileanu, and Maria Lomeli. 2024 · 2024
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Star: Boosting low-resource information extraction by structure-to-text data generation with large language models
Mingyu Derek Ma, Xiaoxuan Wang, Po-Nien Kung, P Jeffrey Brantingham, Nanyun Peng, and Wei Wang. 2024 · 2024
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Self-refine: Iterative refinement with self-feedback
Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, et al. 2024 · 2024
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Peer-review-in-llms: Automatic evaluation method for llms in open-environment
Kun-Peng Ning, Shuo Yang, Yu-Yang Liu, Jia-Yu Yao, Zhen-Hui Liu, Yu Wang, Ming Pang, and Li Yuan. 2024 · 2024
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West-of-n: Synthetic preference generation for improved reward modeling
Alizée Pace, Jonathan Mallinson, Eric Malmi, Sebastian Krause, and Aliaksei Severyn. 2024 · 2024
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Automatically correcting large language models: Surveying the landscape of diverse automated correction strategies
Liangming Pan, Michael Saxon, Wenda Xu, Deepak Nathani, Xinyi Wang, and William Yang Wang. 2024 · 2024
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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. 2024 · 2024
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Towards ontology-enhanced representation learning for large language models
Francesco Ronzano and Jay Nanavati. 2024 · 2024
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Prompting-based synthetic data generation for few-shot question answering
Maximilian Schmidt, Andrea Bartezzaghi, and Ngoc Thang Vu. 2024 · 2024
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Pmg: Personalized multimodal generation with large language models
Xiaoteng Shen, Rui Zhang, Xiaoyan Zhao, Jieming Zhu, and Xi Xiao. 2024 · 2024
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Llm see, llm do: Guiding data generation to target non-differentiable objectives
Luísa Shimabucoro, Sebastian Ruder, Julia Kreutzer, Marzieh Fadaee, and Sara Hooker. 2024 · 2024
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Reflexion: Language agents with verbal reinforcement learning
Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. 2024 · 2024
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Scaling data diversity for fine-tuning language models in human alignment
Feifan Song, Bowen Yu, Hao Lang, Haiyang Yu, Fei Huang, Houfeng Wang, and Yongbin Li. 2024a · 2024
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Best practices for text annotation with large language models
Petter Törnberg. 2024 · 2024
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Are expert-level language models expert-level annotators?
Yu-Min Tseng, Wei-Lin Chen, Chung-Chi Chen, and Hsin-Hsi Chen. 2024 · 2024
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World to code: Multi-modal data generation via self-instructed compositional captioning and filtering
Jiacong Wang, Bohong Wu, Haiyong Jiang, Zhou Xun, Xin Xiao, Haoyuan Guo, and Jun Xiao. 2024c · 2024
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Efficiency in language understanding and generation: An evaluation of four open-source large language models
Siu Ming Wong, Ho Leung, and Ka Yan Wong. 2024 · 2024
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Magpie: Alignment data synthesis from scratch by prompting aligned llms with nothing
Zhangchen Xu, Fengqing Jiang, Luyao Niu, Yuntian Deng, Radha Poovendran, Yejin Choi, and Bill Yuchen Lin. 2024 · 2024
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Towards automating text annotation: A case study on semantic proximity annotation using gpt-4
Sachin Yadav, Tejaswi Choppa, and Dominik Schlechtweg. 2024 · 2024
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Tree of thoughts: Deliberate problem solving with large language models
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Tom Griffiths, Yuan Cao, and Karthik Narasimhan. 2024 · 2024
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Lamm: Language-assisted multi-modal instruction-tuning dataset, framework, and benchmark
Zhenfei Yin, Jiong Wang, Jianjian Cao, Zhelun Shi, Dingning Liu, Mukai Li, Xiaoshui Huang, Zhiyong Wang, Lu Sheng, Lei Bai, et al. 2024 · 2024
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Large language model as attributed training data generator: A tale of diversity and bias
Yue Yu, Yuchen Zhuang, Jieyu Zhang, Yu Meng, Alexander J Ratner, Ranjay Krishna, Jiaming Shen, and Chao Zhang. 2024 · 2024
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Self-rewarding language models
Weizhe Yuan, Richard Yuanzhe Pang, Kyunghyun Cho, Sainbayar Sukhbaatar, Jing Xu, and Jason Weston. 2024 · 2024
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Enhancing human annotation: Leveraging large language models and efficient batch processing
Oleg Zendel, J Shane Culpepper, Falk Scholer, and Paul Thomas. 2024 · 2024
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Learning reward for robot skills using large language models via self-alignment
Yuwei Zeng, Yao Mu, and Lin Shao. 2024 · 2024
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Gimlet: A unified graph-text model for instruction-based molecule zero-shot learning
Haiteng Zhao, Shengchao Liu, Ma Chang, Hannan Xu, Jie Fu, Zhihong Deng, Lingpeng Kong, and Qi Liu. 2024 · 2024
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Enhancing logical reasoning in large language models through graph-based synthetic data
Jiaming Zhou, Abbas Ghaddar, Ge Zhang, Liheng Ma, Yaochen Hu, Soumyasundar Pal, Mark Coates, Bin Wang, Yingxue Zhang, and Jianye Hao. 2024 · 2024
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Fanno: Augmenting high-quality instruction data with open-sourced llms only
He Zhu, Junyou Su, Tianle Lun, Yicheng Tao, Wenjia Zhang, Zipei Fan, and Guanhua Chen. 2024 · 2024
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Optimizing code retrieval: High-quality and scalable dataset annotation through large language models
Rui Li, Qi Liu, Liyang He, Zheng Zhang, Hao Zhang, Shengyu Ye, Junyu Lu, and Zhenya Huang. 2024h · 2065
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