The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
G-SAP: Graph-based Structure-Aware Prompt Learning over Heterogeneous Knowledge for Commonsense Reasoning
Open accessAbstract
Commonsense question answering has demonstrated considerable potential across various applications like assistants and social robots.Although fully fine-tuned Pre-trained Language Model(PLM) has achieved remarkable performance in commonsense reasoning, their tendency to excessively prioritize textual information hampers the precise transfer of structural knowledge and undermines interpretability.Some studies have explored combining Language Models (LM) with Knowledge Graphs (KGs) by coarsely fusing the two modalities to perform Graph Neural Network (GNN)-based reasoning that lacks a profound interaction between heterogeneous modalities.In this paper, we propose a novel Graph-based Structure-Aware Prompt Learning Model for commonsense reasoning, named G-SAP, aiming to maintain a balance between heterogeneous knowledge and enhance the cross-modal interaction within the LM+GNNs model.In particular, an evidence graph is constructed by integrating multiple knowledge sources, i.e.ConceptNet, Wikipedia, and Cambridge Dictionary to boost the performance.Afterward, a structure-aware frozen PLM is employed to fully incorporate the structured and textual information from the evidence graph, where the generation of prompts is driven
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Guilford's Structure of Intellect model provides psychological foundations for categorizing cognitive operations such as pattern recognition, memory retrieval, and evaluation, enhancing reasoning clarity, coherence, and adaptability [562, 195].”
From A Survey of Context Engineering for Large Language Models (2025) · cites this paper as [195]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., & Zettlemoyer, L. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.703 · Cited by 8,821
The Power of Scale for Parameter-Efficient Prompt Tuning
Lester, B., Al-Rfou, R., & Constant, N. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.243 · Cited by 3,096
UNIFIEDQA: Crossing Format Boundaries with a Single QA System
Khashabi, D., Min, S., Khot, T., Sabharwal, A., Tafjord, O., Clark, P., & Hajishirzi, H. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020 · doi:10.18653/v1/2020.findings-emnlp.171 · Cited by 514
Geva, M., Goldberg, Y., & Berant, J. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1107 · Cited by 284
Research works printed but not linked: 40
- Ning Bian, Xianpei Han, Bo Chen, and Le Sun. 2021. Benchmarking knowledgeenhanced commonsense question answering via knowledge-to-text transformation. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 12574--12582.
- Yonatan Bisk, Rowan Zellers, Ronan Le Bras, Jianfeng Gao, and Yejin Choi. 2020. PIQA: Reasoning about Physical Commonsense in Natural Language. (2020), 7432--7439.
- Tom Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared D Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell et al. 2020. Language models are few-shot learners. Advances in neural information processing systems 33 (2020) 1877--1901.
- 10.18653/v1/2020.coling-main.232 · DOI
- Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014). · arXiv
- 10.1609/aimag.v41i4.5304 · DOI
- Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina N Toutanova. 2018. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. CoRR abs/1810.04805 (2018). · arXiv
- Yanlin Feng Xinyue Chen??ò Bill Yuchen Lin Peifeng Wang Jun Yan and Xiang Ren. 2020. Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question Answering. (2020) 1295--1309. · DOI
- Kai Han, An Xiao, Enhua Wu, Jianyuan Guo, Chunjing Xu, and Yunhe Wang. 2021. Transformer in transformer. Advances in Neural Information Processing Systems 34 (2021), 15908--15919.
- Tao He, Lianli Gao, Jingkuan Song, and Yuan-Fang Li. 2022. Towards Open- Vocabulary Scene Graph Generation with Prompt-Based Finetuning. In Computer Vision - ECCV 2022 - 17th European Conference, Tel Aviv, Israel, October 23--27, 2022, Proceedings, Part XXVIII (Lecture Notes in Computer Science, Vol. 13688), Shai Avidan, Gabriel J. Brostow, Moustapha Cissé, Giovanni Maria Farinella, and Tal Hassner (Eds.). Springer, 56--73.
- 10.1109/TNNLS.2021.3129280 · DOI
- N Houlsby, A Giurgiu, S Jastrzkebski, B Morrone, Q de Laroussilhe, A Gesmundo, M Attariyan, and S Gelly. 2019. Parameter-Efficient Transfer Learning for NLP. 97 (2019), 2790--2799.
- 10.18653/v1/2023.acl-long.750 · DOI
- Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova. 2019. Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of naacL-HLT, Vol. 1. 2.
- Wonjae Kim, Bokyung Son, and Ildoo Kim. 2021. Vilt: Vision-and-language transformer without convolution or region supervision. In International Conference on Machine Learning. PMLR, 5583--5594.
- 10.18653/v1/2021.acl-long.353 · DOI · Paper page
- Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. RoBERTa: A Robustly Optimized BERT Pretraining Approach. CoRR abs/1907.11692 (2019). · arXiv
- Nicholas Lourie, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. 2021. UNICORN on RAINBOW: A Universal Commonsense Reasoning Model on a New Multitask Benchmark. (2021), 13480--13488.
- Shangwen Lv Daya Guo Jingjing Xu Duyu Tang Nan Duan Ming Gong Linjun Shou Daxin Jiang Guihong Cao and Songlin Hu. 2020. Graph-Based Reasoning over Heterogeneous External Knowledge for Commonsense Question Answering. (2020) 8449--8456. · DOI
- David Ed Matsumoto. 2009. The Cambridge dictionary of psychology. Cambridge University Press.
- HongyuRen MichihiroYasunaga, AntoineBosselut Percy Liang, and Jure Leskovec. 2021. QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering. (2021), 535--546.
- Todor Mihaylov Peter Clark Tushar Khot and Ashish Sabharwal. 2018. Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering. (2018) 2381--2391. · DOI
- Adam Poliak Jason Naradowsky Aparajita Haldar Rachel Rudinger and Benjamin Van Durme. 2018. Hypothesis Only Baselines in Natural Language Inference. (2018) 180--191. · DOI
- Adam Santoro, David Raposo, David G Barrett, Mateusz Malinowski, Razvan Pascanu, Peter Battaglia, and Timothy Lillicrap. 2017. A simple neural network module for relational reasoning. Advances in neural information processing systems 30 (2017).
- 10.5555/3298023.3298212 · DOI
- Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant. 2019. COMMONSENSEQA: A Question Answering Challenge Targeting Commonsense Knowledge. (2019), 4149--4158.
- Rianne van den Berg, Ivan Titov, and Max Welling. 2018. Modeling Relational Data with Graph Convolutional Networks. 10843 (2018), 593--607.
- Peifeng Wang, Nanyun Peng, Filip Ilievski, Pedro Szekely, and Xiang Ren. 2020. Connecting the Dots: A Knowledgeable Path Generator for Commonsense Question Answering. EMNLP 2020 (2020), 4129--4140.
- Weiqi Wang, Tianqing Fang, Wenxuan Ding, Baixuan Xu, Xin Liu, Yangqiu Song, and Antoine Bosselut. 2023. CAR: Conceptualization-Augmented Reasoner for Zero-Shot Commonsense Question Answering. arXiv preprint arXiv:2305.14869 (2023). · arXiv
- Xiaoyan Wang Pavan Kapanipathi Ryan Musa Mo Yu Kartik Talamadupula Ibrahim Abdelaziz Maria Chang Achille Fokoue Bassem Makni Nicholas Mattei et al. 2019. Improving Natural Language Inference Using External Knowledge in the Science Questions Domain. (2019) 7208--7215. · DOI
- 10.18653/v1/2022.acl-long.292 · DOI
- 10.18653/v1/2023.acl-long.785 · DOI
- 10.1145/1321440.1321449 · DOI
- Yichong Xu, Chenguang Zhu, Ruochen Xu, Yang Liu, Michael Zeng, and Xuedong Huang. 2021. Fusing Context Into Knowledge Graph for Commonsense Question Answering. In Findings of the Association for Computational Linguistics: ACLIJCNLP 2021. 1201--1207.
- Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, and Quoc V Le. 2019. Xlnet: Generalized autoregressive pretraining for language understanding. Advances in neural information processing systems 32 (2019).
- Michihiro Yasunaga, Antoine Bosselut, Hongyu Ren, Xikun Zhang, Christopher D Manning, Percy S Liang, and Jure Leskovec. 2022. Deep bidirectional language knowledge graph pretraining. Advances in Neural Information Processing Systems 35 (2022), 37309--37323.
- 10.1111/mice.12954 · DOI
- 10.1016/j.eswa.2023.120286 · DOI
- Chen Zheng, Parisa Kordjamshidi, Sagnik Ray Choudhury, Nikita Bhutani, and Isabelle Augenstein. 2022. Dynamic Relevance Graph Network for Knowledge- Aware Question Answering. (2022), 1357--1366.
- 10.1007/s11263-022-01653-1 · DOI · Paper page
Cited by
Sorted by citations
A Survey of Context Engineering for Large Language Models
Mei, L., Yao, J., Ge, Y., Wang, Y., Bi, B., Cai, Y., Liu, J., Li, M., Li, Z.-Z., Zhang, D., et al. (2025) · arXiv · doi:10.48550/arxiv.2507.13334 · Cited by 281
Metadata
- Authors
- Dai, R., Tan, Y., Mo, L., Liang, S., Huo, G., Luo, J., & Cheng, Y.
- Affiliations
- University of Electronic Science and Technology of China (CN)
- Venue
- Proceedings of the 2024 International Conference on Multimedia Retrieval
- Publisher
- ACM
- Published
- 2024-05-30
- Pages
- 1051-1060
- OpenAlex
- W4399418463
- Cited by
- 1
- References
- 4
- Citations by year
- 2025: 1
- FWCI
- 0.29
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Conference on Multimedia Retrieval (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Keywords
- commonsense reasoning, commonsense question answering, prompt learning, knowledge graphs, graph neural networks, knowledge heterogeneity, cross-modal interaction, pre-trained language models, evidence graph, ConceptNet, Wikipedia, interpretability
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
