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Large language models (LLMs) are a promising venue for natural language understanding and generation.
Handbook of automated reasoning
Alan JA Robinson and Andrei Voronkov · 2001
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A differential approach to inference in bayesian networks
Adnan Darwiche · 2003
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On probabilistic inference by weighted model counting
Mark Chavira and Adnan Darwiche · 2008
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Maximum satisfiability problem
Roberto Battiti · 2009
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Natural language inference
Bill MacCartney · 2009
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Sdd: A new canonical representation of propositional knowledge bases
Adnan Darwiche · 2011
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Dynamic minimization of sentential decision diagrams
Arthur Choi and Adnan Darwiche · 2013
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A top-down compiler for sentential decision diagrams
Umut Oztok and Adnan Darwiche · 2015
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Sgdr: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2016
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Pointer sentinel mixture models, 2016
Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher · 2016
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Can neural networks understand logical entailment?
Richard Evans, David Saxton, David Amos, Pushmeet Kohli, and Edward Grefenstette · 2018
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Conceptnet 5.5: An open multilingual graph of general knowledge, 2018
Robyn Speer, Joshua Chin, and Catherine Havasi · 2018
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A semantic loss function for deep learning with symbolic knowledge, 2018
Jingyi Xu, Zilu Zhang, Tal Friedman, Yitao Liang, and Guy Van den Broeck · 2018
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Neuro-symbolic = neural + logical + probabilistic
Luc De Raedt, Robin Manhaeve, Sebastijan Dumancic, Thomas Demeester, and Angelika Kimmig · 2019
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Negated and misprimed probes for pretrained language models: Birds can talk, but cannot fly
Nora Kassner and Hinrich Schütze · 2019
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A logic-driven framework for consistency of neural models, 2019
Tao Li, Vivek Gupta, Maitrey Mehta, and Vivek Srikumar · 2019
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Roberta: A robustly optimized bert pretraining approach, 2019
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov · 2019
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Language models as knowledge bases?, 2019
Fabio Petroni, Tim Rocktäschel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, Alexander H. Miller, and Sebastian Riedel · 2019
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Differentiation of blackbox combinatorial solvers
Marin Vlastelica Pogančić, Anselm Paulus, Vit Musil, Georg Martius, and Michal Rolinek · 2019
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Clutrr: A diagnostic benchmark for inductive reasoning from text
Koustuv Sinha, Shagun Sodhani, Jin Dong, Joelle Pineau, and William L Hamilton · 2019
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Tractable probabilistic models: Representations, algorithms, learning, and applications
Antonio Vergari, Nicola Di Mauro, and Guy Van den Broek · 2019
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Efficient generation of structured objects with constrained adversarial networks
Luca Di Liello, Pierfrancesco Ardino, Jacopo Gobbi, Paolo Morettin, Stefano Teso, and Andrea Passerini · 2020
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Coherent hierarchical multi-label classification networks
Eleonora Giunchiglia and Thomas Lukasiewicz · 2020
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Retrieval-augmented generation for knowledge-intensive nlp tasks
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al · 2020
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Differentiation of blackbox combinatorial solvers
Marin Vlastelica Pogancic, Anselm Paulus, Vít Musil, Georg Martius, and Michal Rolínek · 2020
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Fact or fiction: Verifying scientific claims, 2020
David Wadden, Shanchuan Lin, Kyle Lo, Lucy Lu Wang, Madeleine van Zuylen, Arman Cohan, and Hannaneh Hajishirzi · 2020
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On the opportunities and risks of foundation models
Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al · 2021
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From statistical relational to neural-symbolic artificial intelligence
Luc De Raedt, Sebastijan Dumančić, Robin Manhaeve, and Giuseppe Marra · 2021
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Measuring and improving consistency in pretrained language models
Yanai Elazar, Nora Kassner, Shauli Ravfogel, Abhilasha Ravichander, Eduard Hovy, Hinrich Schütze, and Yoav Goldberg · 2021
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Truthful ai: Developing and governing ai that does not lie, 2021
Owain Evans, Owen Cotton-Barratt, Lukas Finnveden, Adam Bales, Avital Balwit, Peter Wills, Luca Righetti, and William Saunders · 2021
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Lora: Low-rank adaptation of large language models, 2021
Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2021
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Beliefbank: Adding memory to a pre-trained language model for a systematic notion of belief, 2021
Nora Kassner, Oyvind Tafjord, Hinrich Schütze, and Peter Clark · 2021
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Survey of hallucination in natural language generation
Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung · 2023
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Language models with rationality, 2023
Nora Kassner, Oyvind Tafjord, Ashish Sabharwal, Kyle Richardson, Hinrich Schuetze, and Peter Clark · 2023
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Vera: A general-purpose plausibility estimation model for commonsense statements, 2023
Jiacheng Liu, Wenya Wang, Dianzhuo Wang, Noah A. Smith, Yejin Choi, and Hannaneh Hajishirzi · 2023
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Ret-llm: Towards a general read-write memory for large language models
Ali Modarressi, Ayyoob Imani, Mohsen Fayyaz, and Hinrich Schütze · 2023
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Ontochatgpt information system: Ontology-driven structured prompts for chatgpt meta-learning
Oleksandr Palagin, Vladislav Kaverinskiy, Anna Litvin, and Kyrylo Malakhov · 2023
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Truthfulqa: Measuring how models mimic human falsehoods, 2021
Stephanie Lin, Jacob Hilton, and Owain Evans · 2021
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Neuro-symbolic forward reasoning
Hikaru Shindo, Devendra Singh Dhami, and Kristian Kersting · 2021
Cited alongside, same era.
General-purpose question-answering with macaw, 2021
Oyvind Tafjord and Peter Clark · 2021
Cited alongside, same era.
A compositional atlas of tractable circuit operations for probabilistic inference
Antonio Vergari, YooJung Choi, Anji Liu, Stefano Teso, and Guy Van den Broeck · 2021
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Semantic Probabilistic Layers for Neuro-Symbolic Learning
Kareem Ahmed, Stefano Teso, Kai-Wei Chang, Guy Van den Broeck, and Antonio Vergari · 2022
Cited alongside, same era.
Neuro-symbolic entropy regularization
Kareem Ahmed, Eric Wang, Kai-Wei Chang, and Guy Van den Broeck · 2022
Cited alongside, same era.
A review on language models as knowledge bases
Badr AlKhamissi, Millicent Li, Asli Celikyilmaz, Mona Diab, and Marjan Ghazvininejad · 2022
Cited alongside, same era.
Later among the works it cites.
Logicbench: A benchmark for evaluation of logical reasoning
Mihir Parmar, Neeraj Varshney, Nisarg Patel, Santosh Mashetty, Man Luo, Arindam Mitra, and Chitta Baral · 2023
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Semantic consistency for assuring reliability of large language models
Harsh Raj, Vipul Gupta, Domenic Rosati, and Subhabrata Majumdar · 2023
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A survey of hallucination in large foundation models
Vipula Rawte, Amit Sheth, and Amitava Das · 2023
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Mededit: Model editing for medical question answering with external knowledge bases
Yucheng Shi, Shaochen Xu, Zhengliang Liu, Tianming Liu, Xiang Li, and Ninghao Liu · 2023
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Fine-tuning language models for factuality, 2023
Katherine Tian, Eric Mitchell, Huaxiu Yao, Christopher D. Manning, and Chelsea Finn · 2023
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Survey on factuality in large language models: Knowledge, retrieval and domain-specificity
Cunxiang Wang, Xiaoze Liu, Yuanhao Yue, Xiangru Tang, Tianhang Zhang, Cheng Jiayang, Yunzhi Yao, Wenyang Gao, Xuming Hu, Zehan Qi, et al · 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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Improved logical reasoning of language models via differentiable symbolic programming, 2023
Hanlin Zhang, Jiani Huang, Ziyang Li, Mayur Naik, and Eric Xing · 2023
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A pseudo-semantic loss for autoregressive models with logical constraints
Kareem Ahmed, Kai-Wei Chang, and Guy Van den Broeck · 2024
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Deductive closure training of language models for coherence, accuracy, and updatability, 2024
Afra Feyza Akyürek, Ekin Akyürek, Leshem Choshen, Derry Wijaya, and Jacob Andreas · 2024
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A benchmark suite for systematically evaluating reasoning shortcuts, 2024
Samuele Bortolotti, Emanuele Marconato, Tommaso Carraro, Paolo Morettin, Emile van Krieken, Antonio Vergari, Stefano Teso, and Andrea Passerini · 2024
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The hallucinations leaderboard–an open effort to measure hallucinations in large language models
Giwon Hong, Aryo Pradipta Gema, Rohit Saxena, Xiaotang Du, Ping Nie, Yu Zhao, Laura Perez-Beltrachini, Max Ryabinin, Xuanli He, and Pasquale Minervini · 2024
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Jiarui Li, Ye Yuan, and Zehua Zhang · 2024
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Flexkbqa: A flexible llm-powered framework for few-shot knowledge base question answering
Zhenyu Li, Sunqi Fan, Yu Gu, Xiuxing Li, Zhichao Duan, Bowen Dong, Ning Liu, and Jianyong Wang · 2024
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Bears make neuro-symbolic models aware of their reasoning shortcuts
Emanuele Marconato, Samuele Bortolotti, Emile van Krieken, Antonio Vergari, Andrea Passerini, and Stefano Teso · 2024
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Not all neuro-symbolic concepts are created equal: Analysis and mitigation of reasoning shortcuts
Emanuele Marconato, Stefano Teso, Antonio Vergari, and Andrea Passerini · 2024
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Memllm: Finetuning llms to use an explicit read-write memory
Ali Modarressi, Abdullatif Köksal, Ayyoob Imani, Mohsen Fayyaz, and Hinrich Schütze · 2024
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Is cosine-similarity of embeddings really about similarity?
Harald Steck, Chaitanya Ekanadham, and Nathan Kallus · 2024
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On the independence assumption in neurosymbolic learning
Emile van Krieken, Pasquale Minervini, Edoardo M Ponti, and Antonio Vergari · 2024
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From instructions to constraints: Language model alignment with automatic constraint verification
Fei Wang, Chao Shang, Sarthak Jain, Shuai Wang, Qiang Ning, Bonan Min, Vittorio Castelli, Yassine Benajiba, and Dan Roth · 2024
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Do large language models latently perform multi-hop reasoning?, 2024
Sohee Yang, Elena Gribovskaya, Nora Kassner, Mor Geva, and Sebastian Riedel · 2024
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