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In recent years, large language models (LLMs) have been widely adopted in political science tasks such as election prediction, sentiment analysis, policy impact assessment, and misinformation detection.
Power and political institutions
Terry M Moe · 2005
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Computational and mathematical modeling in the social sciences
Scott De Marchi · 2005
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An analysis of the 2002 presidential elections using logistic regression
Jairo Nicolau · 2007
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Implicit and explicit prejudice in the 2008 american presidential election
B Keith Payne, Jon A Krosnick, Josh Pasek, Yphtach Lelkes, Omair Akhtar, and Trevor Tompson · 2010
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean · 2013
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Non-state actors in civil wars: A new dataset
David E Cunningham, Kristian Skrede Gleditsch, and Idean Salehyan · 2013
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Separating the wheat from the chaff: Applications of automated document classification using support vector machines
Vito d’Orazio, Steven T Landis, Glenn Palmer, and Philip Schrodt · 2014
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Agent-based models
Scott De Marchi and Scott E Page · 2014
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Large-scale computerized text analysis in political science: Opportunities and challenges
John Wilkerson and Andreu Casas · 2017
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Qualitative methods
John Gerring · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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U.S. President 1976–2020
MIT Election Data and Science Lab · 2017
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Multimodal fusion with recurrent neural networks for rumor detection on microblogs
Zhiwei Jin, Juan Cao, Han Guo, Yongdong Zhang, and Jiebo Luo · 2017
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U.S. Senate statewide 1976–2020
MIT Election Data and Science Lab · 2017
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U.S. House 1976–2022
MIT Election Data and Science Lab · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin · 2018
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An evaluation of the 2016 election polls in the united states
Courtney Kennedy, Mark Blumenthal, Scott Clement, et al · 2018
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Federal Register Final Rule Data 2000-2014
Emily Moore · 2018
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Billsum: A corpus for automatic summarization of us legislation
Anastassia Kornilova and Vlad Eidelman · 2019
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Anes 2016 time series study full release
American National Election Studies · 2019
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Deep learning for political science
Kakia Chatsiou and Slava Jankin Mikhaylov · 2020
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A comparison of methods in political science text classification: Transfer learning language models for politics
Zhanna Terechshenko, Fridolin Linder, Vishakh Padmakumar, Michael Liu, Jonathan Nagler, Joshua A Tucker, and Richard Bonneau · 2020
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A survey on computational politics
Ehsan Ul Haq, Tristan Braud, Young D Kwon, and Pan Hui · 2020
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Language models are few-shot learners, 2020
Tom B. Brown, Benjamin Mann, Nick Ryder, and Others · 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
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What the [mask]? making sense of language-specific bert models
Debora Nozza, Federico Bianchi, and Dirk Hovy · 2020
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Author’s sentiment prediction
Mohaddeseh Bastan, Mahnaz Koupaee, Youngseo Son, Richard Sicoli, and Niranjan Balasubramanian · 2020
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Fakenewsnet: A data repository with news content, social context, and spatiotemporal information for studying fake news on social media
Kai Shu, Deepak Mahudeswaran, Suhang Wang, Dongwon Lee, and Huan Liu · 2020
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Extracting outcomes from appellate decisions in us state courts
Alina Petrova, John Armour, and Thomas Lukasiewicz · 2020
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Editing factual knowledge in language models
Nicola De Cao, Wilker Aziz, and Ivan Titov · 2021
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Ethical and social risks of harm from language models
Laura Weidinger, John Mellor, Maribeth Rauh, Conor Griffin, Jonathan Uesato, Po-Sen Huang, Myra Cheng, Mia Glaese, Borja Balle, Atoosa Kasirzadeh, et al · 2021
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Machine learning for social science: An agnostic approach
Justin Grimmer, Margaret E Roberts, and Brandon M Stewart · 2021
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True few-shot learning with language models
Ethan Perez, Douwe Kiela, and Kyunghyun Cho · 2021
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Efficient large-scale language model training on gpu clusters using megatron-lm
Deepak Narayanan, Mohammad Shoeybi, Jared Casper, et al · 2021
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Generating Synthetic Text Data to Evaluate Causal Inference Methods, February 2021
Zach Wood-Doughty, Ilya Shpitser, and Mark Dredze · 2021
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Finetuned language models are zero-shot learners
Jason Wei, Maarten Bosma, Vincent Y Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, and Quoc V Le · 2021
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“fake news” is not simply false information: A concept explication and taxonomy of online content
Maria D Molina, S Shyam Sundar, Thai Le, and Dongwon Lee · 2021
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Explaining anomalies detected by autoencoders using shapley additive explanations
Liat Antwarg, Ronnie Mindlin Miller, Bracha Shapira, and Lior Rokach · 2021
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Post-war development analysis of political science: from behaviorism to new institutionalism: Political science development trend, challenges and suggestions
Chenxi Gao, Yini Li, et al · 2022
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# election2020: the first public twitter dataset on the 2020 us presidential election
Emily Chen, Ashok Deb, and Emilio Ferrara · 2022
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How to train your stochastic parrot: Large language models for political texts
Joseph T Ornstein, Elise N Blasingame, and Jake S Truscott · 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
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Human-level play in the game of diplomacy by combining language models with strategic reasoning
Meta Fundamental AI Research Diplomacy Team (FAIR)†, Anton Bakhtin, Noam Brown, Emily Dinan, Gabriele Farina, Colin Flaherty, Daniel Fried, Andrew Goff, Jonathan Gray, Hengyuan Hu, et al · 2022
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POLITICS: Pretraining with same-story article comparison for ideology prediction and stance detection
Yujian Liu, Xinliang Frederick Zhang, David Wegsman, Nicholas Beauchamp, and Lu Wang · 2022
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Evaluation of fake news detection with knowledge-enhanced language models
Chenxi Whitehouse, Tillman Weyde, Pranava Madhyastha, and Nikos Komninos · 2022
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Causal inference in natural language processing: Estimation, prediction, interpretation and beyond
Amir Feder, Katherine A Keith, Emaad Manzoor, Reid Pryzant, Dhanya Sridhar, Zach Wood-Doughty, Jacob Eisenstein, Justin Grimmer, Roi Reichart, Margaret E Roberts, et al · 2022
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Ai is mastering language. should we trust what it says?
Steven Johnson and Nikita Iziev · 2022
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Communitylm: Probing partisan worldviews from language models
Hang Jiang, Doug Beeferman, Brandon Roy, and Deb Roy · 2022
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Politics as usual? measuring populism, nationalism, and authoritarianism in us presidential campaigns (1952–2020) with neural language models
Bart Bonikowski, Yuchen Luo, and Oscar Stuhler · 2022
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Survey of aspect-based sentiment analysis datasets
Siva Uday Sampreeth Chebolu, Franck Dernoncourt, Nedim Lipka, and Thamar Solorio · 2022
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Fake news detection on twitter
Srishti Sharma, Mala Saraswat, and Anil Kumar Dubey · 2022
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Sentiment classification in bengali news comments using a hybrid approach with glove
Uchchhwas Saha, Md Shihab Mahmud, Aisharjo Chakrobortty, Mst Tuhin Akter, MD Rakib Islam, and Ahmed Al Marouf · 2022
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Indonesia covid-19 online media news sentiment analysis with lexicon-based approach and emotion detection
Bayu Waspodo, Amalia Khaerunnisa Nursya Bany, Rinda Hesti Kusumaningtyas, Eri Rustamaji, et al · 2022
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U.S. Senate Precinct-Level Returns 2020
MIT Election Data and Science Lab · 2022
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U.S. House of Representatives Precinct-Level Returns 2018
MIT Election Data and Science Lab · 2022
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State Precinct-Level Returns 2018
MIT Election Data and Science Lab · 2022
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A new dataset on legislative decision-making in the european union: the deu iii dataset
Javier Arregui and Clement Perarnaud · 2022
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Public wisdom matters! discourse-aware hyperbolic fourier co-attention for social text classification
Karish Grover, SM Angara, Md Shad Akhtar, and Tanmoy Chakraborty · 2022
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A rigorous study of integrated gradients method and extensions to internal neuron attributions
Daniel D Lundstrom, Tianjian Huang, and Meisam Razaviyayn · 2022
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The shaky foundations of large language models and foundation models for electronic health records
Michael Wornow, Yizhe Xu, Rahul Thapa, Birju Patel, Ethan Steinberg, Scott Fleming, Michael A Pfeffer, Jason Fries, and Nigam H Shah · 2023
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Are large language models ready for healthcare? a comparative study on clinical language understanding
Yuqing Wang, Yun Zhao, and Linda Petzold · 2023
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Finbert: A large language model for extracting information from financial text
Allen H Huang, Hui Wang, and Yi Yang · 2023
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Bloomberggpt: A large language model for finance
Shijie Wu, Ozan Irsoy, Steven Lu, Vadim Dabravolski, Mark Dredze, Sebastian Gehrmann, Prabhanjan Kambadur, David Rosenberg, and Gideon Mann · 2023
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Chatgpt for good? on opportunities and challenges of large language models for education
Enkelejda Kasneci, Kathrin Seßler, Stefan Küchemann, Maria Bannert, Daryna Dementieva, Frank Fischer, Urs Gasser, Georg Groh, Stephan Günnemann, Eyke Hüllermeier, et al · 2023
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Large language models for education: Grading open-ended questions using chatgpt
Gustavo Pinto, Isadora Cardoso-Pereira, Danilo Monteiro, Danilo Lucena, Alberto Souza, and Kiev Gama · 2023
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A survey of large language models
Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, et al · 2023
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Large language models and political science
Mitchell Linegar, Rafal Kocielnik, and R. Michael Alvarez · 2023
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Out of one, many: Using language models to simulate human samples
Lisa P Argyle, Ethan C Busby, Nancy Fulda, Joshua R Gubler, Christopher Rytting, and David Wingate · 2023
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Using large language models in psychology
Dorottya Demszky, Diyi Yang, David S Yeager, Christopher J Bryan, Margarett Clapper, Susannah Chandhok, Johannes C Eichstaedt, Cameron Hecht, Jeremy Jamieson, Meghann Johnson, et al · 2023
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Political bias in large language models
Lucas Gover · 2023
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Petter Törnberg · 2023
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Summary of chatgpt-related research and perspective towards the future of large language models
Yiheng Liu, Tianle Han, Siyuan Ma, Jiayue Zhang, Yuanyuan Yang, Jiaming Tian, Hao He, Antong Li, Mengshen He, Zhengliang Liu, et al · 2023
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Disc-lawllm: Fine-tuning large language models for intelligent legal services
Shengbin Yue, Wei Chen, Siyuan Wang, Bingxuan Li, Chenchen Shen, Shujun Liu, Yuxuan Zhou, Yao Xiao, Song Yun, Xuanjing Huang, et al · 2023
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Simulating social media using large language models to evaluate alternative news feed algorithms
Petter Törnberg, Diliara Valeeva, Justus Uitermark, and Christopher Bail · 2023
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Sentiment analysis in the era of large language models: A reality check
Wenxuan Zhang, Yue Deng, Bing Liu, Sinno Jialin Pan, and Lidong Bing · 2023
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From social media to ballot box: Leveraging location-aware sentiment analysis for election predictions
Asif Khan, Nada Boudjellal, Huaping Zhang, Arshad Ahmad, and Maqbool Khan · 2023
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Whose opinions do language models reflect?
Shibani Santurkar, Esin Durmus, Faisal Ladhak, Cinoo Lee, Percy Liang, and Tatsunori Hashimoto · 2023
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Public opinion on welfare state recalibration in times of austerity: Evidence from survey experiments
Björn Bremer and Reto Bürgisser · 2023
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Inducing political bias allows language models anticipate partisan reactions to controversies
Zihao He, Siyi Guo, Ashwin Rao, and Kristina Lerman · 2023
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Llm lies: Hallucinations are not bugs, but features as adversarial examples
Jia-Yu Yao, Kun-Peng Ning, Zhen-Hui Liu, Mu-Nan Ning, Yu-Yang Liu, and Li Yuan · 2023
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Knowledge editing for large language models: A survey
Song Wang, Yaochen Zhu, Haochen Liu, Zaiyi Zheng, Chen Chen, and Jundong Li · 2023
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Large language models and political science
Mitchell Linegar, Rafal Kocielnik, and R Michael Alvarez · 2023
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Patrick Y Wu, Joshua A Tucker, Jonathan Nagler, and Solomon Messing · 2023
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Demonstrations of the potential of ai-based political issue polling
Nathan E Sanders, Alex Ulinich, and Bruce Schneier · 2023
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Comparing the persuasiveness of role-playing large language models and human experts on polarized us political issues
Kobi Hackenburg, Lujain Ibrahim, Ben M Tappin, and Manos Tsakiris · 2023
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The political biases of chatgpt
David Rozado · 2023
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Llama: Open and efficient foundation language models, 2023
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample · 2023
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Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
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Label supervised llama finetuning
Zongxi Li, Xianming Li, Yuzhang Liu, Haoran Xie, Jing Li, Fu-lee Wang, Qing Li, and Xiaoqin Zhong · 2023
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Instruction tuning for large language models: A survey
Shengyu Zhang, Linfeng Dong, Xiaoya Li, Sen Zhang, Xiaofei Sun, Shuhe Wang, Jiwei Li, Runyi Hu, Tianwei Zhang, Fei Wu, et al · 2023
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In-context retrieval-augmented language models
Ori Ram, Yoav Levine, Itay Dalmedigos, Dor Muhlgay, Amnon Shashua, Kevin Leyton-Brown, and Yoav Shoham · 2023
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A prompt pattern catalog to enhance prompt engineering with chatgpt
Jules White, Quchen Fu, Sam Hays, Michael Sandborn, Carlos Olea, Henry Gilbert, Ashraf Elnashar, Jesse Spencer-Smith, and Douglas C Schmidt · 2023
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Knowledge injection to counter large language model (llm) hallucination
Ariana Martino, Michael Iannelli, and Coleen Truong · 2023
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Parameter-efficient fine-tuning of large-scale pre-trained language models
Ning Ding, Yujia Qin, Guang Yang, Fuchao Wei, Zonghan Yang, Yusheng Su, Shengding Hu, Yulin Chen, Chi-Min Chan, Weize Chen, et al · 2023
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Fast distributed inference serving for large language models
Bingyang Wu, Yinmin Zhong, Zili Zhang, Shengyu Liu, Fangyue Liu, Yuanhang Sun, Gang Huang, Xuanzhe Liu, and Xin Jin · 2023
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On the effectiveness of parameter-efficient fine-tuning
Zihao Fu, Haoran Yang, Anthony Man-Cho So, Wai Lam, Lidong Bing, and Nigel Collier · 2023
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Efficient memory management for large language model serving with pagedattention
Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph Gonzalez, Hao Zhang, and Ion Stoica · 2023
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A survey of controllable text generation using transformer-based pre-trained language models
Hanqing Zhang, Haolin Song, Shaoyu Li, Ming Zhou, and Dawei Song · 2023
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How great is the current danger to democracy? assessing the risk with historical data
Daniel Treisman · 2023
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Can you label less by using out-of-domain data? active & transfer learning with few-shot instructions
Rafal Kocielnik, Sara Kangaslahti, Shrimai Prabhumoye, Meena Hari, Michael Alvarez, and Anima Anandkumar · 2023
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Generative ai and the future of elections, 2023
R Michael Alvarez, Frederick Eberhardt, and Mitchell Linegar · 2023
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Large language models can argue in convincing and novel ways about politics: Evidence from experiments and human judgement
Alexis Palmer and Arthur Spirling · 2023
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Melora: Mini-ensemble low-rank adapters for parameter-efficient fine-tuning
Pengjie Ren, Chengshun Shi, Shiguang Wu, Mengqi Zhang, Zhaochun Ren, Maarten Rijke, Zhumin Chen, and Jiahuan Pei · 2024
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Hidden persuaders: Llms’ political leaning and their influence on voters, 2024
Yujin Potter, Shiyang Lai, Junsol Kim, James Evans, and Dawn Song · 2024
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(a) i am not a lawyer, but…: Engaging legal experts towards responsible llm policies for legal advice
Inyoung Cheong, King Xia, KJ Kevin Feng, Quan Ze Chen, and Amy X Zhang · 2024
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Fake news in sheep’s clothing: Robust fake news detection against llm-empowered style attacks
Jiaying Wu, Jiafeng Guo, and Bryan Hooi · 2024
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Leveraging knowledge graphs and llms to support and monitor legislative systems
Andrea Colombo · 2024
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Generative agents: Interactive simulacra of human behavior
Joon Sung Park, Joseph O’Brien, Carrie Jun Cai, Meredith Ringel Morris, Percy Liang, and Michael S Bernstein · 2023
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Large language models empowered agent-based modeling and simulation: A survey and perspectives
Chen Gao, Xiaochong Lan, Nian Li, Yuan Yuan, Jingtao Ding, Zhilun Zhou, Fengli Xu, and Yong Li · 2023
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War and peace (waragent): Large language model-based multi-agent simulation of world wars
Wenyue Hua, Lizhou Fan, Lingyao Li, Kai Mei, Jianchao Ji, Yingqiang Ge, Libby Hemphill, and Yongfeng Zhang · 2023
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Simulating opinion dynamics with networks of llm-based agents
Yun-Shiuan Chuang, Agam Goyal, Nikunj Harlalka, Siddharth Suresh, Robert Hawkins, Sijia Yang, Dhavan Shah, Junjie Hu, and Timothy T Rogers · 2023
Cited alongside, same era.
Causal parrots: Large language models may talk causality but are not causal
Matej Zečević, Moritz Willig, Devendra Singh Dhami, and Kristian Kersting · 2023
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The Challenge of Using LLMs to Simulate Human Behavior: A Causal Inference Perspective
George Gui and Olivier Toubia · 2023
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The rise of chatbots in political campaigns: The effects of conversational agents on voting intention
Yunju Kim and Heejun Lee · 2023
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Using imperfect surrogates for downstream inference: Design-based supervised learning for social science applications of large language models
Naoki Egami, Musashi Hinck, Brandon Stewart, and Hanying Wei · 2024
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Llama meets EU: Investigating the European political spectrum through the lens of LLMs
Ilias Chalkidis and Stephanie Brandl · 2024
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Can large language models detect misinformation in scientific news reporting?
Yupeng Cao, Aishwarya Muralidharan Nair, Elyon Eyimife, Nastaran Jamalipour Soofi, KP Subbalakshmi, John R Wullert II, Chumki Basu, and David Shallcross · 2024
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Evaluating large language models for user stance detection on x (twitter)
Margherita Gambini, Caterina Senette, Tiziano Fagni, and Maurizio Tesconi · 2024
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Explainable Fake News Detection with Large Language Model via Defense Among Competing Wisdom
Bo Wang, Jing Ma, Hongzhan Lin, Zhiwei Yang, Ruichao Yang, Yuan Tian, and Yi Chang · 2024
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Fake News in Sheep’s Clothing: Robust Fake News Detection Against LLM-Empowered Style Attacks
Jiaying Wu, Jiafeng Guo, and Bryan Hooi · 2024
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Bad actor, good advisor: Exploring the role of large language models in fake news detection
Beizhe Hu, Qiang Sheng, Juan Cao, Yuhui Shi, Yang Li, Danding Wang, and Peng Qi · 2024
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Corporate opposition to climate change disclosure regulation in the united states
Addisu Lashitew and Youqing Mu · 2024
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Deciphering public voices in the digital era: Benchmarking chatgpt for analyzing citizen feedback in hamilton, new zealand
Xinyu Fu, Thomas W Sanchez, Chaosu Li, and Juliana Reu Junqueira · 2024
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Measuring executive agency ideology using large language models
Nicholas G Napolio · 2024
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Synthetic replacements for human survey data? the perils of large language models
James Bisbee, Joshua D Clinton, Cassy Dorff, Brenton Kenkel, and Jennifer M Larson · 2024
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Evaluating the quality of answers in political q&a sessions with large language models
R Michael Alvarez and Jacob Morrier · 2024
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Do ais know what the most important issue is? using language models to code open-text social survey responses at scale
Jonathan Mellon, Jack Bailey, Ralph Scott, James Breckwoldt, Marta Miori, and Phillip Schmedeman · 2024
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A survey on large language model based autonomous agents
Lei Wang, Chen Ma, Xueyang Feng, Zeyu Zhang, Hao Yang, Jingsen Zhang, Zhiyuan Chen, Jiakai Tang, Xu Chen, Yankai Lin, et al · 2024
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What if llms have different world views: Simulating alien civilizations with llm-based agents
Mingyu Jin, Beichen Wang, Zhaoqian Xue, Suiyuan Zhu, Wenyue Hua, Hua Tang, Kai Mei, Mengnan Du, and Yongfeng Zhang · 2024
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Richelieu: Self-evolving llm-based agents for ai diplomacy
Zhenyu Guan, Xiangyu Kong, Fangwei Zhong, and Yizhou Wang · 2024
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Modelling political coalition negotiations using llm-based agents
Farhad Moghimifar, Yuan-Fang Li, Robert Thomson, and Gholamreza Haffari · 2024
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Cause and Effect: Can Large Language Models Truly Understand Causality?, 2024
Swagata Ashwani, Kshiteesh Hegde, Nishith Reddy Mannuru, Mayank Jindal, Dushyant Singh Sengar, Krishna Chaitanya Rao Kathala, Dishant Banga, Vinija Jain, and Aman Chadha · 2024
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Causal Reasoning and Large Language Models: Opening a New Frontier for Causality, 2024
Emre Kıcıman, Robert Ness, Amit Sharma, and Chenhao Tan · 2024
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Prompting Large Language Models for Counterfactual Generation: An Empirical Study, 2024
Yongqi Li, Mayi Xu, Xin Miao, Shen Zhou, and Tieyun Qian · 2024
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Zero-shot LLM-guided Counterfactual Generation for Text, 2024
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Explainability for large language models: A survey
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The use of machine learning methods in political science: An in-depth literature review
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End-to-end causal effect estimation from unstructured natural language data
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Large language models portray socially subordinate groups as more homogeneous, consistent with a bias observed in humans
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Evaluating the persuasive influence of political microtargeting with large language models
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The use of ai by election campaigns
Florian Foos · 2024
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Will trump win in 2024? predicting the us presidential election via multi-step reasoning with large language models, 2024
Chenxiao Yu, Zhaotian Weng, Zheng Li, Xiyang Hu, and Yue Zhao · 2024
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Lawllm: Law large language model for the us legal system
Dong Shu, Haoran Zhao, Xukun Liu, David Demeter, Mengnan Du, and Yongfeng Zhang · 2024
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Leak, cheat, repeat: Data contamination and evaluation malpractices in closed-source llms
Simone Balloccu, Patrícia Schmidtová, Mateusz Lango, and Ondřej Dušek · 2024
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Large language models for data annotation and synthesis: A survey
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Autolabel: Automated textual data annotation method based on active learning and large language model
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Performance and biases of large language models in public opinion simulation
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Synthetic oversampling: Theory and a practical approach using llms to address data imbalance
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Identifying citizen-related issues from social media using llm-based data augmentation
Vitor Gaboardi dos Santos, Guto Leoni Santos, Theo Lynn, and Boualem Benatallah · 2024
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Data augmentation using llms: Data perspectives, learning paradigms and challenges
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When scaling meets llm finetuning: The effect of data, model and finetuning method
Biao Zhang, Zhongtao Liu, Colin Cherry, and Orhan Firat · 2024
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Fine tuning llm for enterprise: Practical guidelines and recommendations
Kushala VM, Harikrishna Warrier, Yogesh Gupta, et al · 2024
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Multitask learning for crash analysis: A fine-tuned llm framework using twitter data
Shadi Jaradat, Richi Nayak, Alexander Paz, Huthaifa I Ashqar, and Mohammad Elhenawy · 2024
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Novel-wd: Exploring acquisition of novel world knowledge in llms using prefix-tuning
Maxime Méloux and Christophe Cerisara · 2024
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Acco: Accumulate while you communicate, hiding communications in distributed llm training
Adel Nabli, Louis Fournier, Pierre Erbacher, Louis Serrano, Eugene Belilovsky, and Edouard Oyallon · 2024
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Aptq: Attention-aware post-training mixed-precision quantization for large language models
Ziyi Guan, Hantao Huang, Yupeng Su, Hong Huang, Ngai Wong, and Hao Yu · 2024
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Lexsumm and lext5: Benchmarking and modeling legal summarization tasks in english
TYSS Santosh, Cornelius Weiss, and Matthias Grabmair · 2024
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Legalbench: A collaboratively built benchmark for measuring legal reasoning in large language models
Neel Guha, Julian Nyarko, Daniel Ho, Christopher Ré, Adam Chilton, Alex Chohlas-Wood, Austin Peters, Brandon Waldon, Daniel Rockmore, Diego Zambrano, et al · 2024
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Mapping (a) ideology: A taxonomy of european parties using generative llms as zero-shot learners
Riccardo Di Leo, Chen Zeng, Elias Dinas, and Reda Tamtam · 2024
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Dynamic few-shot learning for computational social science
Ranadheer Malla, Travis G Coan, Vivek Srinivasan, and Constantine Boussalis · 2024
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Alapan Kuila and Sudeshna Sarkar · 2024
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Political debate: Efficient zero-shot and few-shot classifiers for political text
Michael Burnham, Kayla Kahn, Ryan Yank Wang, and Rachel X Peng · 2024
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Enhancing zero-shot crypto sentiment with fine-tuned language model and prompt engineering
Rahman SM Wahidur, Ishmam Tashdeed, Manjit Kaur, and Heung-No Lee · 2024
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More samples or more prompts? exploring effective few-shot in-context learning for llms with in-context sampling
Bingsheng Yao, Guiming Chen, Ruishi Zou, Yuxuan Lu, Jiachen Li, Shao Zhang, Yisi Sang, Sijia Liu, James Hendler, and Dakuo Wang · 2024
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Analyzing political stances on twitter in the lead-up to the 2024 us election
Hazem Ibrahim, Farhan Khan, Hend Alabdouli, Maryam Almatrooshi, Tran Nguyen, Talal Rahwan, and Yasir Zaki · 2024
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Under the influence: A survey of large language models in fake news detection
Soveatin Kuntur, Anna Wróblewska, Marcin Paprzycki, and Maria Ganzha · 2024
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Bohdan M Pavlyshenko · 2024
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A multi-modal prompt learning framework for early detection of fake news
Weiqi Hu, Ye Wang, Yan Jia, Qing Liao, and Bin Zhou · 2024
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A fine-grained self-adapting prompt learning approach for few-shot learning with pre-trained language models
Xiaojun Chen, Ting Liu, Philippe Fournier-Viger, Bowen Zhang, Guodong Long, and Qin Zhang · 2024
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Evaluating quality of answers for retrieval-augmented generation: A strong llm is all you need
Yang Wang, Alberto Garcia Hernandez, Roman Kyslyi, and Nicholas Kersting · 2024
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Understand what llm needs: Dual preference alignment for retrieval-augmented generation
Guanting Dong, Yutao Zhu, Chenghao Zhang, Zechen Wang, Zhicheng Dou, and Ji-Rong Wen · 2024
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Political-rag: using generative ai to extract political information from media content
Muhammad Arslan, Saba Munawar, and Christophe Cruz · 2024
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Generating chain-of-thoughts with a pairwise-comparison approach to searching for the most promising intermediate thought
Zhen-Yu Zhang, Siwei Han, Huaxiu Yao, Gang Niu, and Masashi Sugiyama · 2024
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Leveraging Large Language Models for Classifying Subjective Arguments in Public Discourse
Adina Dobrinoiu · 2024
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A comprehensive study of knowledge editing for large language models
Ningyu Zhang, Yunzhi Yao, Bozhong Tian, Peng Wang, Shumin Deng, Mengru Wang, Zekun Xi, Shengyu Mao, Jintian Zhang, Yuansheng Ni, et al · 2024
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Stackfeed: Structured textual actor-critic knowledge base editing with feedback
Naman Gupta, Shashank Kirtania, Priyanshu Gupta, and Others · 2024
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Oneedit: A neural-symbolic collaboratively knowledge editing system
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Hao Peng, Xiaozhi Wang, Chunyang Li, Kaisheng Zeng, Jiangshan Duo, Yixin Cao, Lei Hou, and Juanzi Li · 2024
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Integrative decoding: Improve factuality via implicit self-consistency
Yi Cheng, Xiao Liang, Yeyun Gong, Wen Xiao, Song Wang, Yuji Zhang, Wenjun Hou, Kaishuai Xu, Wenge Liu, Wenjie Li, et al · 2024
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Gpt-4o: The cutting-edge advancement in multimodal llm
Raisa Islam and Owana Marzia Moushi · 2024
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Areeg Fahad Rasheed, M Zarkoosh, Safa F Abbas, and Sana Sabah Al-Azzawi · 2024
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Jian Chen, Vashisth Tiwari, Ranajoy Sadhukhan, Zhuoming Chen, Jinyuan Shi, Ian En-Hsu Yen, and Beidi Chen · 2024
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Scidqa: A deep reading comprehension dataset over scientific papers
Shruti Singh, Nandan Sarkar, and Arman Cohan · 2024
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Representation bias in political sample simulations with large language models
Weihong Qi, Hanjia Lyu, and Jiebo Luo · 2024
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Generative ai voting: Fair collective choice is resilient to llm biases and inconsistencies
Srijoni Majumdar, Edith Elkind, and Evangelos Pournaras · 2024
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The power of llm-generated synthetic data for stance detection in online political discussions
Stefan Sylvius Wagner, Maike Behrendt, Marc Ziegele, and Stefan Harmeling · 2024
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Social science meets llms: How reliable are large language models in social simulations?
Yue Huang, Zhengqing Yuan, Yujun Zhou, Kehan Guo, Xiangqi Wang, Haomin Zhuang, Weixiang Sun, Lichao Sun, Jindong Wang, Yanfang Ye, et al · 2024
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Deductive verification of chain-of-thought reasoning
Zhan Ling, Yunhao Fang, Xuanlin Li, Zhiao Huang, Mingu Lee, Roland Memisevic, and Hao Su · 2024
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Canyu Chen, Baixiang Huang, Zekun Li, Zhaorun Chen, Shiyang Lai, Xiongxiao Xu, Jia-Chen Gu, Jindong Gu, Huaxiu Yao, Chaowei Xiao, et al · 2024
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Measuring political bias in large language models: What is said and how it is said
Yejin Bang, Delong Chen, Nayeon Lee, and Pascale Fung · 2024
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Twin-gpt: Digital twins for clinical trials via large language model
Yue Wang, Yinlong Xu, Zihan Ma, Hongxia Xu, Bang Du, Honghao Gao, Jian Wu, and Jintai Chen · 2024
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Comal: Collaborative multi-agent large language models for mixed-autonomy traffic
Huaiyuan Yao, Longchao Da, Vishnu Nandam, Justin Turnau, Zhiwei Liu, Linsey Pang, and Hua Wei · 2024
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C2P: Featuring Large Language Models with Causal Reasoning, 2024
Abdolmahdi Bagheri, Matin Alinejad, Kevin Bello, and Alireza Akhondi-Asl · 2024
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Efficient Causal Graph Discovery Using Large Language Models, 2024
Thomas Jiralerspong, Xiaoyin Chen, Yash More, Vedant Shah, and Yoshua Bengio · 2024
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Conspiracy narratives on voat: A longitudinal analysis of cognitive activation and evolutionary psychology features
Veronika Batzdorfer · 2024
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Cause and effect: Can large language models truly understand causality?
Swagata Ashwani, Kshiteesh Hegde, Nishith Reddy Mannuru, Dushyant Singh Sengar, Mayank Jindal, Krishna Chaitanya Rao Kathala, Dishant Banga, Vinija Jain, and Aman Chadha · 2024
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What makes a high-quality training dataset for large language models: A practitioners’ perspective
Xiao Yu, Zexian Zhang, Feifei Niu, Xing Hu, Xin Xia, and John Grundy · 2024
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Haocheng Lin · 2024
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Chen Huang, Yang Deng, Wenqiang Lei, Jiancheng Lv, and Ido Dagan · 2024
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Red and blue language: Word choices in the trump & harris 2024 presidential debate
Philipp Wicke and Marianna M Bolognesi · 2024
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M Abdul Khaliq, P Chang, M Ma, Bernhard Pflugfelder, and F Miletić · 2024
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Longchao Da, Tiejin Chen, Lu Cheng, and Hua Wei · 2024
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