Fetching the paper…
Reading the bibliography…
A persistent challenge in AI is the effective integration of material and formal inference - the former concerning the plausibility and contextual relevance of arguments, while the latter focusing on their logical and structural validity.
Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych. 2019 · 1908
Earlier work this paper cites.
SemEval-2024 task 2: Safe biomedical natural language inference for clinical trials
Mael Jullien, Marco Valentino, and André Freitas. 2024 · 1962
Earlier work this paper cites.
Philosophy of Logics
Susan Haack. 1978 · 1978
Earlier work this paper cites.
The best explanation: Criteria for theory choice
Paul R Thagard. 1978 · 1978
Earlier work this paper cites.
Making it explicit: Reasoning, representing, and discursive commitment
Robert Brandom. 1994 · 1994
Earlier work this paper cites.
Okapi at trec-3
Stephen E Robertson, Steve Walker, Susan Jones, Micheline M Hancock-Beaulieu, Mike Gatford, and 1 others. 1995 · 1995
Earlier work this paper cites.
Isabelle/HOL: a proof assistant for higher-order logic
Tobias Nipkow, Markus Wenzel, and Lawrence C Paulson. 2002 · 2002
Earlier work this paper cites.
Learning reasoning strategies in end-to-end differentiable proving
Pasquale Minervini, Sebastian Riedel, Pontus Stenetorp, Edward Grefenstette, and Tim Rocktäschel. 2020 · 2007
Earlier work this paper cites.
The probabilistic relevance framework: Bm25 and beyond
Stephen Robertson, Hugo Zaragoza, and 1 others. 2009 · 2009
Earlier work this paper cites.
SemEval-2012 task 7: Choice of plausible alternatives: An evaluation of commonsense causal reasoning
Andrew Gordon, Zornitsa Kozareva, and Melissa Roemmele. 2012 · 2012
Earlier work this paper cites.
Argumentation TheoryArgumentationtheory , pages 1–49
Frans H. van Eemeren, Bart Garssen, Erik C. W. Krabbe, A. Francisca Snoeck Henkemans, Bart Verheij, and Jean H. M. Wagemans. 2014 · 2014
Earlier work this paper cites.
Inference to the best explanation
Peter Lipton. 2017 · 2017
Earlier work this paper cites.
End-to-end differentiable proving
Tim Rocktäschel and Sebastian Riedel. 2017 · 2017
Earlier work this paper cites.
e-snli: Natural language inference with natural language explanations
Oana-Maria Camburu, Tim Rocktäschel, Thomas Lukasiewicz, and Phil Blunsom. 2018 · 2018
Earlier work this paper cites.
Worldtree: A corpus of explanation graphs for elementary science questions supporting multi-hop inference
Peter Jansen, Elizabeth Wainwright, Steven Marmorstein, and Clayton Morrison. 2018 · 2018
Earlier work this paper cites.
Deepproblog: Neural probabilistic logic programming
Robin Manhaeve, Sebastijan Dumancic, Angelika Kimmig, Thomas Demeester, and Luc De Raedt. 2018 · 2018
Earlier work this paper cites.
NLProlog: Reasoning with weak unification for question answering in natural language
Leon Weber, Pasquale Minervini, Jannes Münchmeyer, Ulf Leser, and Tim Rocktäschel. 2019 · 2019
Earlier work this paper cites.
Natural language premise selection: Finding supporting statements for mathematical text
Deborah Ferreira and André Freitas. 2020 · 2020
Cited alongside, same era.
TextGraphs 2020 Shared Task on Multi-Hop Inference for Explanation Regeneration
Peter Jansen and Dmitry Ustalov. 2020 · 2020
Cited alongside, same era.
Braid: Weaving symbolic and neural knowledge into coherent logical explanations
Aditya Kalyanpur, Tom Breloff, and David A. Ferrucci. 2020 · 2020
Cited alongside, same era.
Explaining answers with entailment trees
Bhavana Dalvi, Peter Jansen, Oyvind Tafjord, Zhengnan Xie, Hannah Smith, Leighanna Pipatanangkura, and Peter Clark. 2021 · 2021
Cited alongside, same era.
Measuring sentence-level and aspect-level (un) certainty in science communications
Jiaxin Pei and David Jurgens. 2021 · 2021
Cited alongside, same era.
Neural natural logic inference for interpretable question answering
Inference to the best explanation in large language models
Dhairya Dalal, Marco Valentino, Andre Freitas, and Paul Buitelaar. 2024 · 2024
Later among the works it cites.
Inductive learning of logical theories with llms: A complexity-graded analysis
Joao Pedro Gandarela, Danilo S. Carvalho, and Andr’e Freitas. 2024 · 2024
Later among the works it cites.
Is neuro-symbolic ai meeting its promises in natural language processing? a structured review
Kyle Hamilton, Aparna Nayak, Bojan Božić, and Luca Longo. 2024 · 2024
Later among the works it cites.
LeanReasoner: Boosting complex logical reasoning with lean
Dongwei Jiang, Marcio Fonseca, and Shay Cohen. 2024 · 2024
Later among the works it cites.
Position: Llms can’t plan, but can help planning in llm-modulo frameworks
Subbarao Kambhampati, Karthik Valmeekam, Lin Guan, Mudit Verma, Kaya Stechly, Siddhant Bhambri, Lucas Paul Saldyt, and Anil B Murthy. 2024 · 2024
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Jihao Shi, Xiao Ding, Li Du, Ting Liu, and Bing Qin. 2021 · 2021
Cited alongside, same era.
TextGraphs 2021 shared task on multi-hop inference for explanation regeneration
Mokanarangan Thayaparan, Marco Valentino, Peter Jansen, and Dmitry Ustalov. 2021 · 2021
Cited alongside, same era.
Language models show human-like content effects on reasoning tasks
Ishita Dasgupta, Andrew K Lampinen, Stephanie CY Chan, Hannah R Sheahan, Antonia Creswell, Dharshan Kumaran, James L McClelland, and Felix Hill. 2022 · 2022
Cited alongside, same era.
Diff-explainer: Differentiable convex optimization for explainable multi-hop inference
Mokanarangan Thayaparan, Marco Valentino, Deborah Ferreira, Julia Rozanova, and André Freitas. 2022 · 2022
Cited alongside, same era.
TextGraphs 2022 shared task on natural language premise selection
Marco Valentino, Deborah Ferreira, Mokanarangan Thayaparan, André Freitas, and Dmitry Ustalov. 2022a · 2022
Cited alongside, same era.
Deep inductive logic reasoning for multi-hop reading comprehension
Wenya Wang and Sinno Pan. 2022 · 2022
Cited alongside, same era.
Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, and 1 others. 2023 · 2023
Cited alongside, same era.
LOGIC-LM++: Multi-step refinement for symbolic formulations
Shashank Kirtania, Priyanshu Gupta, and Arjun Radhakrishna. 2024 · 2024
Later among the works it cites.
Dissociating language and thought in large language models
Kyle Mahowald, Anna A Ivanova, Idan A Blank, Nancy Kanwisher, Joshua B Tenenbaum, and Evelina Fedorenko. 2024 · 2024
Later among the works it cites.
Enhancing reasoning capabilities of llms via principled synthetic logic corpus
Terufumi Morishita, Gaku Morio, Atsuki Yamaguchi, and Yasuhiro Sogawa. 2024 · 2024
Later among the works it cites.
Verification and refinement of natural language explanations through LLM-symbolic theorem proving
Xin Quan, Marco Valentino, Louise A. Dennis, and Andre Freitas. 2024b · 2024
Later among the works it cites.
Reasoning with natural language explanations
Marco Valentino and André Freitas. 2024b · 2024
Later among the works it cites.
Nellie: A neuro-symbolic inference engine for grounded, compositional, and explainable reasoning
Nathaniel Weir, Peter Clark, and Benjamin Van Durme. 2024 · 2024
Later among the works it cites.
Faithful logical reasoning via symbolic chain-of-thought
Jundong Xu, Hao Fei, Liangming Pan, Qian Liu, Mong-Li Lee, and Wynne Hsu. 2024 · 2024
Later among the works it cites.
Empowering llms with logical reasoning: A comprehensive survey
Fengxiang Cheng, Haoxuan Li, Fenrong Liu, Robert van Rooij, Kun Zhang, and Zhouchen Lin. 2025 · 2025
Closest in time.
Inductive learning of logical theories with llms: An expressivity-graded analysis
João Pedro Gandarela, Danilo S. Carvalho, and André Freitas. 2025 · 2025
Closest in time.
Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning
Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, and 1 others. 2025 · 2025
Closest in time.
Improving chain-of-thought reasoning via quasi-symbolic abstractions
Leonardo Ranaldi, Marco Valentino, Alexander Polonsky, and André Freitas. 2025 · 2025
Closest in time.