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Causal inference has shown potential in enhancing the predictive accuracy, fairness, robustness, and explainability of Natural Language Processing (NLP) models by capturing causal relationships among variables.
Language models are few-shot learners
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Roberta: A robustly optimized bert pretraining approach
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Certified robustness to adversarial word substitutions
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The method of path coefficients
Sewall Wright. 1934 · 1934
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Estimating causal effects of treatments in randomized and nonrandomized studies
Donald B Rubin. 1974 · 1974
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Graphical models for probabilistic and causal reasoning
Judea Pearl. 1998 · 1998
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Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei. 2020 · 2001
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Exploiting cloze questions for few shot text classification and natural language inference
Timo Schick and Hinrich Schütze. 2020 · 2001
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Macer: Attack-free and scalable robust training via maximizing certified radius
Runtian Zhai, Chen Dan, Di He, Huan Zhang, Boqing Gong, Pradeep Ravikumar, Cho-Jui Hsieh, and Liwei Wang. 2020 · 2001
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Causality
Judea Pearl. 2009 · 2009
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Measurement bias and effect restoration in causal inference
Manabu Kuroki and Judea Pearl. 2014 · 2014
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Commonsense reasoning and commonsense knowledge in artificial intelligence
Ernest Davis and Gary Marcus. 2015 · 2015
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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 · 2017
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A survey of methods for explaining black box models
Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti, and Dino Pedreschi. 2018 · 2018
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The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery
Zachary C Lipton. 2018 · 2018
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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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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019 · 2019
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Evaluating gender bias in machine translation
Gabriel Stanovsky, Noah A. Smith, and Luke Zettlemoyer. 2019 · 2019
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BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020 · 2020
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Investigating gender bias in language models using causal mediation analysis
Jesse Vig, Sebastian Gehrmann, Yonatan Belinkov, Sharon Qian, Daniel Nevo, Yaron Singer, and Stuart Shieber. 2020 · 2020
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A review of dataset and labeling methods for causality extraction
Jinghang Xu, Wanli Zuo, Shining Liang, and Xianglin Zuo. 2020 · 2020
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Rongzhou Bao, Jiayi Wang, and Hai Zhao. 2021 · 2021
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An empirical survey of the effectiveness of debiasing techniques for pre-trained language models
Nicholas Meade, Elinor Poole-Dayan, and Siva Reddy. 2021 · 2021
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Zhao Meng, Yihan Dong, Mrinmaya Sachan, and Roger Wattenhofer. 2021 · 2021
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Counterfactual vqa: A cause-effect look at language bias
Yulei Niu, Kaihua Tang, Hanwang Zhang, Zhiwu Lu, Xian-Sheng Hua, and Ji-Rong Wen. 2021 · 2021
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Can prompt probe pretrained language models? understanding the invisible risks from a causal view
Boxi Cao, Hongyu Lin, Xianpei Han, Fangchao Liu, and Le Sun. 2022 · 2022
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Palm: Scaling language modeling with pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, Parker Schuh, Kensen Shi, Sasha Tsvyashchenko, Joshua Maynez, Abhishek Rao, Parker Barnes, Yi Tay, Noam M. Shazeer, Vinodkumar Prabhakaran, Emily Reif, Nan Du, Ben Hutchinson, Reiner Pope, James Bradbury, Jacob Austin, Michael Isard, Guy Gur-Ari, Pengcheng Yin, Toju Duke, Anselm Levskaya, Sanjay Ghemawat, Sunipa Dev, Henryk Michalewski, Xavier García, Vedant Misra, Kevin Robinson, Liam Fedus, Denny Zhou, Daphne Ippolito, David Luan, Hyeontaek Lim, Barret Zoph, Alexander Spiridonov, Ryan Sepassi, David Dohan, Shivani Agrawal, Mark Omernick, Andrew M. Dai, Thanumalayan Sankaranarayana Pillai, Marie Pellat, Aitor Lewkowycz, Erica Moreira, Rewon Child, Oleksandr Polozov, Katherine Lee, Zongwei Zhou, Xuezhi Wang, Brennan Saeta, Mark Díaz, Orhan Firat, Michele Catasta, Jason Wei, Kathleen S. Meier-Hellstern, Douglas Eck, Jeff Dean, Slav Petrov, and Noah Fiedel. 2022 · 2022
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Word embeddings via causal inference: Gender bias reducing and semantic information preserving
Lei Ding, Dengdeng Yu, Jinhan Xie, Wenxing Guo, Shenggang Hu, Meichen Liu, Linglong Kong, Hongsheng Dai, Yanchun Bao, and Bei Jiang. 2022 · 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, Brandon M. Stewart, Victor Veitch, and Diyi Yang. 2022 · 2022
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Holistic evaluation of language models
Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Kumar, et al. 2022 · 2022
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Neuro-symbolic procedural planning with commonsense prompting
Yujie Lu, Weixi Feng, Wanrong Zhu, Wenda Xu, Xin Eric Wang, Miguel Eckstein, and William Yang Wang. 2022 · 2022
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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
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Language prior is not the only shortcut: A benchmark for shortcut learning in vqa
Qingyi Si, Fandong Meng, Mingyu Zheng, Zheng Lin, Yuanxin Liu, Peng Fu, Yanan Cao, Weiping Wang, and Jie Zhou. 2022 · 2022
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Lamda: Language models for dialog applications
Romal Thoppilan, Daniel De Freitas, Jamie Hall, Noam Shazeer, Apoorv Kulshreshtha, Heng-Tze Cheng, Alicia Jin, Taylor Bos, Leslie Baker, Yu Du, et al. 2022 · 2022
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Probing for correlations of causal facts: Large language models and causality
Moritz Willig, Matej Zečević, Devendra Singh Dhami, and Kristian Kersting. 2022 · 2022
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A survey on extraction of causal relations from natural language text
Jie Yang, Soyeon Caren Han, and Josiah Poon. 2022 · 2022
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A survey on causal discovery: theory and practice
Alessio Zanga, Elif Ozkirimli, and Fabio Stella. 2022 · 2022
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A survey of causal inference frameworks
Jingying Zeng and Run Wang. 2022 · 2022
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Rock: Causal inference principles for reasoning about commonsense causality
Jiayao Zhang, Hongming Zhang, Weijie Su, and Dan Roth. 2022 · 2022
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Certified robustness against natural language attacks by causal intervention
Haiteng Zhao, Chang Ma, Xinshuai Dong, Anh Tuan Luu, Zhi-Hong Deng, and Hanwang Zhang. 2022 · 2022
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Extracting self-consistent causal insights from users feedback with llms and in-context learning
Crab: Assessing the strength of causal relationships between real-world events
Angelika Romanou, Syrielle Montariol, Debjit Paul, Leo Laugier, Karl Aberer, and Antoine Bosselut. 2023 · 2023
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Indira Sen, Dennis Assenmacher, Mattia Samory, Isabelle Augenstein, Wil van der Aalst, and Claudia Wagne. 2023 · 2023
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Alessandro Stolfo, Yonatan Belinkov, and Mrinmaya Sachan. 2023 · 2023
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Language models are causal knowledge extractors for zero-shot video question answering
Hung-Ting Su, Yulei Niu, Xudong Lin, Winston H Hsu, and Shih-Fu Chang. 2023 · 2023
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alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Sara Abdali, Anjali Parikh, Steve Lim, and Emre Kiciman. 2023 · 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 · 2023
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Zero-shot causal graph extrapolation from text via llms
Alessandro Antonucci, Gregorio Piqu’e, and Marco Zaffalon. 2023 · 2023
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Large language models for biomedical causal graph construction
Vahan Arsenyan and Davit Shahnazaryan. 2023 · 2023
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Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, et al. 2023 · 2023
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Eliciting latent predictions from transformers with the tuned lens
Nora Belrose, Zach Furman, Logan Smith, Danny Halawi, Igor Ostrovsky, Lev McKinney, Stella Biderman, and Jacob Steinhardt. 2023 · 2023
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Relevance-based infilling for natural language counterfactuals
Lorenzo Betti, Carlo Abrate, Francesco Bonchi, and Andreas Kaltenbrunner. 2023 · 2023
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Yan Tai, Weichen Fan, Zhao Zhang, Feng Zhu, Rui Zhao, and Ziwei Liu. 2023 · 2023
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Causal abstraction for chain-of-thought reasoning in arithmetic word problems
Juanhe TJ Tan. 2023 · 2023
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Ziyi Tang, Ruilin Wang, Weixing Chen, Keze Wang, Yang Liu, Tianshui Chen, and Liang Lin. 2023 · 2023
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Alpaca: A strong, replicable instruction-following model
Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B Hashimoto. 2023 · 2023
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Gemini: a family of highly capable multimodal models
Gemini Team, Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, et al. 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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Causal-discovery performance of chatgpt in the context of neuropathic pain diagnosis
Ruibo Tu, Chao Ma, and Cheng Zhang. 2023 · 2023
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Causal inference using llm-guided discovery
Aniket Vashishtha, Abbavaram Gowtham Reddy, Abhinav Kumar, Saketh Bachu, Vineeth N Balasubramanian, and Amit Sharma. 2023 · 2023
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Biasasker: Measuring the bias in conversational ai system
Yuxuan Wan, Wenxuan Wang, Pinjia He, Jiazhen Gu, Haonan Bai, and Michael R Lyu. 2023 · 2023
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A causal view of entity bias in (large) language models
Fei Wang, Wenjie Mo, Yiwei Wang, Wenxuan Zhou, and Muhao Chen. 2023 · 2023
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Interpretability at scale: Identifying causal mechanisms in alpaca
Zhengxuan Wu, Atticus Geiger, Christopher Potts, and Noah D Goodman. 2023 · 2023
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Large language models can be good privacy protection learners
Yijia Xiao, Yiqiao Jin, Yushi Bai, Yue Wu, Xianjun Yang, Xiao Luo, Wenchao Yu, Xujiang Zhao, Yanchi Liu, Haifeng Chen, et al. 2023 · 2023
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Echo: Event causality inference via human-centric reasoning
Yuxi Xie, Guanzhen Li, and Min-Yen Kan. 2023 · 2023
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Ifqa: A dataset for open-domain question answering under counterfactual presuppositions
Wenhao Yu, Meng Jiang, Peter Clark, and Ashish Sabharwal. 2023 · 2023
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Causal parrots: Large language models may talk causality but are not causal
Matej Zečević, Moritz Willig, Devendra Singh Dhami, and Kristian Kersting. 2023 · 2023
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Preserving commonsense knowledge from pre-trained language models via causal inference
Junhao Zheng, Qianli Ma, Shengjie Qiu, Yue Wu, Peitian Ma, Junlong Liu, Huawen Feng, Xichen Shang, and Haibin Chen. 2023 · 2023
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Quantifying and mitigating unimodal biases in multimodal large language models: A causal perspective
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