Fetching the paper…
Reading the bibliography…
We introduce Graph of Thoughts (GoT): a framework that advances prompting capabilities in large language models (LLMs) beyond those offered by paradigms such as Chain-of-Thought or Tree of Thoughts (ToT).
Language Models are Few-Shot Learners
Brown, T.; Mann, B.; Ryder, N.; Subbiah, M.; Kaplan, J. D.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; Agarwal, S.; Herbert-Voss, A.; Krueger, G.; Henighan, T.; Child, R.; Ramesh, A.; Ziegler, D.; Wu, J.; Winter, C.; Hesse, C.; Chen, M.; Sigler, E.; Litwin, M.; Gray, S.; Chess, B.; Clark, J.; Berner, C.; McCandlish, S.; Radford, A.; Sutskever, I.; and Amodei, D. 2020 · 1901
Earlier work this paper cites.
GraphMineSuite: Enabling High-Performance and Programmable Graph Mining Algorithms with Set Algebra
Besta, M.; Vonarburg-Shmaria, Z.; Schaffner, Y.; Schwarz, L.; Kwaśniewski, G.; Gianinazzi, L.; Beranek, J.; Janda, K.; Holenstein, T.; Leisinger, S.; Tatkowski, P.; Ozdemir, E.; Balla, A.; Copik, M.; Lindenberger, P.; Konieczny, M.; Mutlu, O.; and Hoefler, T. 2021b · 1935
Earlier work this paper cites.
Cyclic Pattern Kernels for Predictive Graph Mining
Horváth, T.; Gärtner, T.; and Wrobel, S. 2004 · 2004
Earlier work this paper cites.
Machine Learning on Graphs: A Model and Comprehensive Taxonomy
Chami, I.; Abu-El-Haija, S.; Perozzi, B.; Ré, C.; and Murphy, K. 2020 · 2005
Earlier work this paper cites.
Graph Mining: Laws, Generators, and Algorithms
Chakrabarti, D.; and Faloutsos, C. 2006 · 2006
Earlier work this paper cites.
Mining Graph Data
Cook, D. J.; and Holder, L. B., eds. 2006 · 2006
Earlier work this paper cites.
Challenges in Parallel Graph Processing
Lumsdaine, A.; Gregor, D.; Hendrickson, B.; and Berry, J. 2007 · 2007
Earlier work this paper cites.
Graph clustering
Schaeffer, S. E. 2007 · 2007
Earlier work this paper cites.
Fast Graph Pattern Matching
Cheng, J.; Yu, J. X.; Ding, B.; Philip, S. Y.; and Wang, H. 2008 · 2008
Earlier work this paper cites.
Hierarchical Models in the Brain
Friston, K. 2008 · 2008
Earlier work this paper cites.
The Graph Neural Network Model
Scarselli, F.; Gori, M.; Tsoi, A. C.; Hagenbuchner, M.; and Monfardini, G. 2008 · 2008
Earlier work this paper cites.
Graph Pattern Matching: From Intractable to Polynomial Time
Fan, W.; Li, J.; Ma, S.; Tang, N.; Wu, Y.; and Wu, Y. 2010 · 2010
Earlier work this paper cites.
Pregel: A System for Large-Scale Graph Processing
Malewicz, G.; Austern, M. H.; Bik, A. J.; Dehnert, J. C.; Horn, I.; Leiser, N.; and Czajkowski, G. 2010 · 2010
Earlier work this paper cites.
AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts
Shin, T.; Razeghi, Y.; Logan IV, R. L.; Wallace, E.; and Singh, S. 2020 · 2010
Earlier work this paper cites.
Triangles to Capture Social Cohesion
Friggeri, A.; Chelius, G.; and Fleury, E. 2011 · 2011
Earlier work this paper cites.
Shaping Communities out of Triangles
Prat-Pérez, A.; Dominguez-Sal, D.; Brunat, J. M.; and Larriba-Pey, J.-L. 2012 · 2012
Earlier work this paper cites.
A survey of frequent subgraph mining algorithms
Jiang, C.; Coenen, F.; and Zito, M. 2013 · 2013
Earlier work this paper cites.
DISTINGER: A distributed graph data structure for massive dynamic graph processing
Feng, G.; Meng, X.; and Ammar, K. 2015 · 2015
Earlier work this paper cites.
Graph Databases: New Opportunities for Connected Data
Robinson, I.; Webber, J.; and Eifrem, E. 2015 · 2015
Earlier work this paper cites.
Arabesque: A System for Distributed Graph Mining
Teixeira, C. H. C.; Fonseca, A. J.; Serafini, M.; Siganos, G.; Zaki, M. J.; and Aboulnaga, A. 2015 · 2015
Earlier work this paper cites.
Geometric Deep Learning: Going beyond Euclidean data
Bronstein, M. M.; Bruna, J.; LeCun, Y.; Szlam, A.; and Vandergheynst, P. 2017 · 2017
Earlier work this paper cites.
Representation Learning on Graphs: Methods and Applications
Hamilton, W. L.; Ying, R.; and Leskovec, J. 2017 · 2017
Earlier work this paper cites.
Attention is All you Need
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, Ł.; and Polosukhin, I. 2017 · 2017
Cited alongside, same era.
Low-Latency Graph Streaming Using Compressed Purely-Functional Trees
Dhulipala, L.; Blelloch, G. E.; and Shun, J. 2019 · 2019
Cited alongside, same era.
Communication-Efficient Jaccard Similarity for High-Performance Distributed Genome Comparisons
Besta, M.; Kanakagiri, R.; Mustafa, H.; Karasikov, M.; Rätsch, G.; Hoefler, T.; and Solomonik, E. 2020 · 2020
Cited alongside, same era.
Graph neural networks: A review of methods and applications
Zhou, J.; Cui, G.; Hu, S.; Zhang, Z.; Yang, C.; Liu, Z.; Wang, L.; Li, C.; and Sun, M. 2020 · 2020
Cited alongside, same era.
Learning Combinatorial Node Labeling Algorithms
Gianinazzi, L.; Fries, M.; Dryden, N.; Ben-Nun, T.; Besta, M.; and Hoefler, T. 2021 · 2021
Cited alongside, same era.
STaR: Bootstrapping Reasoning With Reasoning
Zelikman, E.; Wu, Y.; Mu, J.; and Goodman, N. 2022 · 2022
Later among the works it cites.
Deep Learning on Graphs: A Survey
Zhang, Z.; Cui, P.; and Zhu, W. 2022 · 2022
Later among the works it cites.
Large Language Models Are Human-Level Prompt Engineers
Zhou, Y.; Muresanu, A. I.; Han, Z.; Paster, K.; Pitis, S.; Chan, H.; and Ba, J. 2022 · 2022
Later among the works it cites.
GDI: A Graph Database Interface Standard
Besta, M.; Gerstenberger, R.; Blach, N.; Fischer, M.; and Hoefler, T. 2023b · 2023
Closest in time.
Sparks of Artificial General Intelligence: Early experiments with GPT-4
Bubeck, S.; Chandrasekaran, V.; Eldan, R.; Gehrke, J.; Horvitz, E.; Kamar, E.; Lee, P.; Lee, Y. T.; Li, Y.; Lundberg, S.; Nori, H.; Palangi, H.; Ribeiro, M. T.; and Zhang, Y. 2023 · 2023
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Explanation-Based Human Debugging of NLP Models: A Survey
Lertvittayakumjorn, P.; and Toni, F. 2021 · 2021
Cited alongside, same era.
The Power of Scale for Parameter-Efficient Prompt Tuning
Lester, B.; Al-Rfou, R.; and Constant, N. 2021 · 2021
Cited alongside, same era.
Prefix-Tuning: Optimizing Continuous Prompts for Generation
Li, X. L.; and Liang, P. 2021 · 2021
Cited alongside, same era.
Show Your Work: Scratchpads for Intermediate Computation with Language Models
Nye, M.; Andreassen, A. J.; Gur-Ari, G.; Michalewski, H.; Austin, J.; Bieber, D.; Dohan, D.; Lewkowycz, A.; Bosma, M.; Luan, D.; Sutton, C.; and Odena, A. 2021 · 2021
Cited alongside, same era.
The Future is Big Graphs: A Community View on Graph Processing Systems
Sakr, S.; Bonifati, A.; Voigt, H.; Iosup, A.; Ammar, K.; Angles, R.; Aref, W.; Arenas, M.; Besta, M.; Boncz, P. A.; Daudjee, K.; Valle, E. D.; Dumbrava, S.; Hartig, O.; Haslhofer, B.; Hegeman, T.; Hidders, J.; Hose, K.; Iamnitchi, A.; Kalavri, V.; Kapp, H.; Martens, W.; Özsu, M. T.; Peukert, E.; Plantikow, S.; Ragab, M.; Ripeanu, M. R.; Salihoglu, S.; Schulz, C.; Selmer, P.; Sequeda, J. F.; Shinavier, J.; Szárnyas, G.; Tommasini, R.; Tumeo, A.; Uta, A.; Varbanescu, A. L.; Wu, H.-Y.; Yakovets, N.; Yan, D.; and Yoneki, E. 2021 · 2021
Cited alongside, same era.
Putting Humans in the Natural Language Processing Loop: A Survey
Wang, Z. J.; Choi, D.; Xu, S.; and Yang, D. 2021 · 2021
Cited alongside, same era.
A Comprehensive Survey on Graph Neural Networks
Wu, Z.; Pan, S.; Chen, F.; Long, G.; Zhang, C.; and Yu, P. S. 2021 · 2021
Cited alongside, same era.
Chen, X.; Lin, M.; Schärli, N.; and Zhou, D. 2023 · 2023
Closest in time.
Language Models can Solve Computer Tasks
Kim, G.; Baldi, P.; and McAleer, S. 2023 · 2023
Closest in time.
Large Language Model Guided Tree-of-Thought
Long, J. 2023 · 2023
Closest in time.
Self-Refine: Iterative Refinement with Self-Feedback
Madaan, A.; Tandon, N.; Gupta, P.; Hallinan, S.; Gao, L.; Wiegreffe, S.; Alon, U.; Dziri, N.; Prabhumoye, S.; Yang, Y.; Gupta, S.; Majumder, B. P.; Hermann, K.; Welleck, S.; Yazdanbakhsh, A.; and Clark, P. 2023 · 2023
Closest in time.
Skeleton-of-Thought: Large Language Models Can Do Parallel Decoding
Ning, X.; Lin, Z.; Zhou, Z.; Wang, Z.; Yang, H.; and Wang, Y. 2023 · 2023
Closest in time.
REFINER: Reasoning Feedback on Intermediate Representations
Paul, D.; Ismayilzada, M.; Peyrard, M.; Borges, B.; Bosselut, A.; West, R.; and Faltings, B. 2023 · 2023
Closest in time.
Reasoning with Language Model Prompting: A Survey
Qiao, S.; Ou, Y.; Zhang, N.; Chen, X.; Yao, Y.; Deng, S.; Tan, C.; Huang, F.; and Chen, H. 2023 · 2023
Closest in time.
graph-of-thoughts Repository
qrdlgit. 2023 · 2023
Closest in time.
Improving Language Understanding by Generative Pre-Training
Radford, A.; Narasimhan, K.; Salimans, T.; and Sutskever, I. 2018 · 2023
Closest in time.
Language Models are Unsupervised Multitask Learners
Radford, A.; Wu, J.; Child, R.; Luan, D.; Amodei, D.; and Sutskever, I. 2019 · 2023
Closest in time.
Reflexion: Language Agents with Verbal Reinforcement Learning
Shinn, N.; Labash, B.; and Gopinath, A. 2023 · 2023
Closest in time.
Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Data
Shum, K.; Diao, S.; and Zhang, T. 2023 · 2023
Closest in time.
Self-Evaluation Guided Beam Search for Reasoning
Xie, Y.; Kawaguchi, K.; Zhao, Y.; Zhao, X.; Kan, M.-Y.; He, J.; and Xie, Q. 2023 · 2023
Closest in time.
Foundation Models for Decision Making: Problems, Methods, and Opportunities
Yang, S.; Nachum, O.; Du, Y.; Wei, J.; Abbeel, P.; and Schuurmans, D. 2023 · 2023
Closest in time.
Beyond Chain-of-Thought, Effective Graph-of-Thought Reasoning in Large Language Models
Yao, Y.; Li, Z.; and Zhao, H. 2023 · 2023
Closest in time.
Planning with Large Language Models for Code Generation
Zhang, S.; Chen, Z.; Shen, Y.; Ding, M.; Tenenbaum, J. B.; and Gan, C. 2023 · 2023
Closest in time.
Solving Math Word Problems via Cooperative Reasoning induced Language Models
Zhu, X.; Wang, J.; Zhang, L.; Zhang, Y.; Huang, Y.; Gan, R.; Zhang, J.; and Yang, Y. 2023 · 2023
Closest in time.