---
title: "Execution as Verification — paper by Zhang et al. (2026)"
description: "Their EVER framework builds the query incrementally. 2.8 points. 43 references. Among them: 0."
authors: ["Minghan Zhang", "Zhen Yang", "Haodong Zou", "Jie Chen", "Zhen Duan", "Shu Zhao"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.1747/"
doi: "10.18653/v1/2026.acl-long.1747"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/zhang-2026-execution-as-verification-fine-grained/"
---
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# Execution as Verification: Fine-Grained Self-Correcting Reasoning for Complex KBQA

**Authors:** [Minghan Zhang](https://md.research.elamur.ai/free/en/authors/minghan-zhang/), [Zhen Yang](https://md.research.elamur.ai/free/en/authors/zhen-yang/), [Haodong Zou](https://md.research.elamur.ai/free/en/authors/haodong-zou/), [Jie Chen](https://md.research.elamur.ai/free/en/authors/jie-chen/), [Zhen Duan](https://md.research.elamur.ai/free/en/authors/zhen-duan/) and [Shu Zhao](https://md.research.elamur.ai/free/en/authors/shu-zhao/) · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.acl-long.1747/) · [PDF](https://aclanthology.org/2026.acl-long.1747.pdf) · [DOI 10.18653/v1/2026.acl-long.1747](https://doi.org/10.18653/v1/2026.acl-long.1747) · [OpenAlex](https://openalex.org/W7166798982)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
LLM-based semantic parsing for KBQA usually writes a complete logical form in one pass and only then runs it. This is fragile: a tiny syntax slip makes the query unexecutable, and even runnable queries can encode the wrong meaning, such as a wrong entity or relation. The authors note that for systems like ChatKBQA, successful execution and answer correctness agree about 90% of the time. They therefore use execution against the knowledge base as a verification signal.

Their EVER framework builds the query incrementally. Each atomic sub-query is produced by a hybrid decoder, where greedy decoding fixes the step's intent and beam search then explores executable skeletons. Each sub-query is executed at once. A correction module then checks entities, relation intent and result types, rolling back and swapping candidates when needed. The model is fine-tuned on instruction data that mixes correct chains with synthetic error-repair trajectories, such as injected entity ambiguity.

On WebQSP and CWQ, EVER reports state-of-the-art results, gaining most on long multi-hop questions. This makes interpretable symbolic KBQA more robust.

## Key points
- On CWQ, atomic sub-queries execute successfully 97.3% of the time, and final query executability reaches 94.5%. That is 2.8 points above ChatKBQA's one-shot generation.
- The advantage grows with question complexity. On the hardest CWQ questions (≥6 relations in the logical form), EVER gains 9.4 F1 points over ChatKBQA.
- The self-correction step triggers on 14.5% of sub-queries on WebQSP and 20.2% on CWQ. It successfully repairs 69.5% and 58.8% of those cases, respectively.
- Most repairs take a single retry (86.7% on WebQSP, 79.9% on CWQ), which keeps the extra cost moderate. Even so, EVER is slower than one-shot methods.
- A stated limitation is that the model cannot easily tell an empty result caused by a semantic error from one caused by gaps in the knowledge base. The code is released at github.com/ahu-zmh/EVER.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Anhui University](https://md.research.elamur.ai/free/en/universities/anhui-university/) — University, Hefei, China · 11 works · 7 citations

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## Cite

Cite as: Minghan Zhang, Zhen Yang, Haodong Zou, Jie Chen, Zhen Duan and Shu Zhao. 2026. Execution as Verification: Fine-Grained Self-Correcting Reasoning for Complex KBQA. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{zhang2026execution,
  title = {{Execution as Verification: Fine-Grained Self-Correcting Reasoning for Complex KBQA}},
  author = {Minghan Zhang, Zhen Yang, Haodong Zou, Jie Chen, Zhen Duan and Shu Zhao},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.1747},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1747}
}
```

- [← previous EXCEEDS: Extracting Complex Events via Nugget-based Grid Modeling in Scientific Domain](https://md.research.elamur.ai/free/en/papers/lu-2026-exceeds-extracting-complex-events-nugget/)
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- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/32/)

## References

43 references, 12 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Kurt D. Bollacker, Colin Evans, Praveen K. Paritosh, Tim Sturge, and Jamie Taylor. 2008. Freebase: a collaboratively created graph database for structuring human knowledge. In Proceedings of the ACM SIG-MOD International Conference on Management of Data, SIGMOD 2008, Vancouver, BC, Canada, June 10-1… [find](https://scholar.google.com/scholar?q=Kurt+D%2E+Bollacker%2C+Colin+Evans%2C+Praveen+K%2E+Paritosh%2C+Tim+Sturge%2C+and+Jamie+Taylor%2E+2008%2E+Freebase%3A+a+collaboratively+created+graph+database+for+structuring+human+knowledge%2E+In+Proceedings+of+the+ACM+S)
2. Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter,… — [Language Models are Few-Shot Learners](https://md.research.elamur.ai/free/en/papers/brown-2020-language-models-few-shot-learners/)
3. Shulin Cao, Jiaxin Shi, Zijun Yao, Xin Lv, Jifan Yu, Lei Hou, Juanzi Li, Zhiyuan Liu, and Jinghui Xiao. 2022. Program transfer for answering complex questions over knowledge bases. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL… [find](https://scholar.google.com/scholar?q=Shulin+Cao%2C+Jiaxin+Shi%2C+Zijun+Yao%2C+Xin+Lv%2C+Jifan+Yu%2C+Lei+Hou%2C+Juanzi+Li%2C+Zhiyuan+Liu%2C+and+Jinghui+Xiao%2E+2022%2E+Program+transfer+for+answering+complex+questions+over+knowledge+bases%2E+In+Proceedings+of+t)
4. Huajun Chen. 2024. Large knowledge model: Perspectives and challenges. DATA INTELLIGENCE, 6(3):587–620. [find](https://scholar.google.com/scholar?q=Huajun+Chen%2E+2024%2E+Large+knowledge+model%3A+Perspectives+and+challenges%2E+DATA+INTELLIGENCE%2C+6%283%29%3A587%E2%80%93620%2E)
5. Liyi Chen, Panrong Tong, Zhongming Jin, Ying Sun, Jieping Ye, and Hui Xiong. 2024. Plan-on-graph: Self-correcting adaptive planning of large language model on knowledge graphs. In Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, N… [find](https://scholar.google.com/scholar?q=Liyi+Chen%2C+Panrong+Tong%2C+Zhongming+Jin%2C+Ying+Sun%2C+Jieping+Ye%2C+and+Hui+Xiong%2E+2024%2E+Plan%2Don%2Dgraph%3A+Self%2Dcorrecting+adaptive+planning+of+large+language+model+on+knowledge+graphs%2E+In+Advances+in+Neural+I)
6. Haishuo Fang, Xiaodan Zhu, and Iryna Gurevych. 2024. DARA: decomposition-alignment-reasoning autonomous language agent for question answering over knowledge graphs. In Findings of the Association for Computational Linguistics, ACL 2024, Bangkok, Thailand and virtual meeting, August 11-16, 2024, page… [find](https://scholar.google.com/scholar?q=Haishuo+Fang%2C+Xiaodan+Zhu%2C+and+Iryna+Gurevych%2E+2024%2E+DARA%3A+decomposition%2Dalignment%2Dreasoning+autonomous+language+agent+for+question+answering+over+knowledge+graphs%2E+In+Findings+of+the+Association+for+)
7. Tengfei Feng and Liang He. 2025. [RGR-KBQA: generating logical forms for question answering using knowledge-graph-enhanced large language model](https://md.research.elamur.ai/free/en/papers/feng-2025-rgr-kbqa-generating-logical-forms/). In Proceedings of the 31st International Conference on Computational Linguistics, COLING 2025, Abu Dhabi, UAE, January 19-24, 2025, pages 3057–3070. Associa…
8. Evgeniy Gabrilovich, Michael Ringgaard, and Amarnag Subramanya. 2013. Facc1: Freebase annotation of clueweb corpora, version 1 (release date 2013-06-26, format version 1, correction level 0). [find](https://scholar.google.com/scholar?q=Evgeniy+Gabrilovich%2C+Michael+Ringgaard%2C+and+Amarnag+Subramanya%2E+2013%2E+Facc1%3A+Freebase+annotation+of+clueweb+corpora%2C+version+1+%28release+date+2013%2D06%2D26%2C+format+version+1%2C+correction+level+0%29%2E)
9. Tianyu Gao, Xingcheng Yao, and Danqi Chen. 2021. [Simcse: Simple contrastive learning of sentence embeddings](https://md.research.elamur.ai/free/en/papers/gao-2021-simcse-simple-contrastive-learning-sentence/). In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021, Virtual Event / Punta Cana, Dominican Republic, 7-11 November, 2021, pages 6894– 6910. A…
10. Yu Gu, Sue Kase, Michelle Vanni, Brian M. Sadler, Percy Liang, Xifeng Yan, and Yu Su. 2021. Beyond I.I.D.: three levels of generalization for question answering on knowledge bases. In WWW ’21: The Web Conference 2021, Virtual Event / Ljubljana, Slovenia, April 19-23, 2021, pages 3477–3488. ACM / IW3… [find](https://scholar.google.com/scholar?q=Yu+Gu%2C+Sue+Kase%2C+Michelle+Vanni%2C+Brian+M%2E+Sadler%2C+Percy+Liang%2C+Xifeng+Yan%2C+and+Yu+Su%2E+2021%2E+Beyond+I%2EI%2ED%2E%3A+three+levels+of+generalization+for+question+answering+on+knowledge+bases%2E+In+WWW+%E2%80%9921%3A+The+Web)
11. Wangzhen Guo, Linyin Luo, Hanjiang Lai, and Jian Yin. 2023. From parse-execute to parse-execute-refine: Improving semantic parser for complex question answering over knowledge base. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023, Singapore, Dece… [find](https://scholar.google.com/scholar?q=Wangzhen+Guo%2C+Linyin+Luo%2C+Hanjiang+Lai%2C+and+Jian+Yin%2E+2023%2E+From+parse%2Dexecute+to+parse%2Dexecute%2Drefine%3A+Improving+semantic+parser+for+complex+question+answering+over+knowledge+base%2E+In+Proceedings+of+)
12. Gaole He, Yunshi Lan, Jing Jiang, Wayne Xin Zhao, and Ji-Rong Wen. 2021. Improving multi-hop knowledge base question answering by learning intermediate supervision signals. In WSDM ’21, The Fourteenth ACM International Conference on Web Search and Data Mining, Virtual Event, Israel, March 8-12, 2021… [find](https://scholar.google.com/scholar?q=Gaole+He%2C+Yunshi+Lan%2C+Jing+Jiang%2C+Wayne+Xin+Zhao%2C+and+Ji%2DRong+Wen%2E+2021%2E+Improving+multi%2Dhop+knowledge+base+question+answering+by+learning+intermediate+supervision+signals%2E+In+WSDM+%E2%80%9921%2C+The+Fourteenth)
13. Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Yejin Bang, Andrea Madotto, and Pascale Fung. 2023. [Survey of hallucination in natural language generation](https://md.research.elamur.ai/free/en/papers/ji-2022-survey-hallucination-natural-language-generation/). ACM Comput. Surv., 55(12):248:1–248:38.
14. Jinhao Jiang, Kun Zhou, Zican Dong, Keming Ye, Xin Zhao, and Ji-Rong Wen. 2023a. Structgpt: A general framework for large language model to reason over structured data. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023, Singapore, December 6-10, 20… [find](https://scholar.google.com/scholar?q=Jinhao+Jiang%2C+Kun+Zhou%2C+Zican+Dong%2C+Keming+Ye%2C+Xin+Zhao%2C+and+Ji%2DRong+Wen%2E+2023a%2E+Structgpt%3A+A+general+framework+for+large+language+model+to+reason+over+structured+data%2E+In+Proceedings+of+the+2023+Conf)
15. Jinhao Jiang, Kun Zhou, Xin Zhao, and Ji-Rong Wen. 2023b. Unikgqa: Unified retrieval and reasoning for solving multi-hop question answering over knowledge graph. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenReview.net. [find](https://scholar.google.com/scholar?q=Jinhao+Jiang%2C+Kun+Zhou%2C+Xin+Zhao%2C+and+Ji%2DRong+Wen%2E+2023b%2E+Unikgqa%3A+Unified+retrieval+and+reasoning+for+solving+multi%2Dhop+question+answering+over+knowledge+graph%2E+In+The+Eleventh+International+Conferen)
16. Yunshi Lan, Gaole He, Jinhao Jiang, Jing Jiang, Wayne Xin Zhao, and Ji-Rong Wen. 2021. A survey on complex knowledge base question answering: Methods, challenges and solutions. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI 2021, Virtual Event / Mont… [find](https://scholar.google.com/scholar?q=Yunshi+Lan%2C+Gaole+He%2C+Jinhao+Jiang%2C+Jing+Jiang%2C+Wayne+Xin+Zhao%2C+and+Ji%2DRong+Wen%2E+2021%2E+A+survey+on+complex+knowledge+base+question+answering%3A+Methods%2C+challenges+and+solutions%2E+In+Proceedings+of+the+T)
17. Yunshi Lan and Jing Jiang. 2020. Query graph generation for answering multi-hop complex questions from knowledge bases. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, July 5-10, 2020, pages 969–974. Association for Computational Linguist… [find](https://scholar.google.com/scholar?q=Yunshi+Lan+and+Jing+Jiang%2E+2020%2E+Query+graph+generation+for+answering+multi%2Dhop+complex+questions+from+knowledge+bases%2E+In+Proceedings+of+the+58th+Annual+Meeting+of+the+Association+for+Computational+L)
18. Tianle Li, Xueguang Ma, Alex Zhuang, Yu Gu, Yu Su, and Wenhu Chen. 2023. Few-shot in-context learning on knowledge base question answering. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2023, Toronto, Canada, July 9-14, 2023,… [find](https://scholar.google.com/scholar?q=Tianle+Li%2C+Xueguang+Ma%2C+Alex+Zhuang%2C+Yu+Gu%2C+Yu+Su%2C+and+Wenhu+Chen%2E+2023%2E+Few%2Dshot+in%2Dcontext+learning+on+knowledge+base+question+answering%2E+In+Proceedings+of+the+61st+Annual+Meeting+of+the+Association)
19. Xujian Liang and Zhaoquan Gu. 2025. Fast think-on-graph: Wider, deeper and faster reasoning of large language model on knowledge graph. In AAAI-25, Sponsored by the Association for the Advancement of Artificial Intelligence, February 25 - March 4, 2025, Philadelphia, PA, USA, pages 24558–24566. AAAI… [find](https://scholar.google.com/scholar?q=Xujian+Liang+and+Zhaoquan+Gu%2E+2025%2E+Fast+think%2Don%2Dgraph%3A+Wider%2C+deeper+and+faster+reasoning+of+large+language+model+on+knowledge+graph%2E+In+AAAI%2D25%2C+Sponsored+by+the+Association+for+the+Advancement+of+)
20. Xiao Long, Liansheng Zhuang, Aodi Li, Minghong Yao, and Shafei Wang. 2025. EPERM: an evidence path enhanced reasoning model for knowledge graph question and answering. In AAAI-25, Sponsored by the Association for the Advancement of Artificial Intelligence, February 25 - March 4, 2025, Philadelphia,… [find](https://scholar.google.com/scholar?q=Xiao+Long%2C+Liansheng+Zhuang%2C+Aodi+Li%2C+Minghong+Yao%2C+and+Shafei+Wang%2E+2025%2E+EPERM%3A+an+evidence+path+enhanced+reasoning+model+for+knowledge+graph+question+and+answering%2E+In+AAAI%2D25%2C+Sponsored+by+the+Ass)
21. Haoran Luo, Haihong E, Zichen Tang, Shiyao Peng, Yikai Guo, Wentai Zhang, Chenghao Ma, Guanting Dong, Meina Song, Wei Lin, Yifan Zhu, and Anh Tuan Luu. 2024a. Chatkbqa: A generate-then-retrieve framework for knowledge base question answering with fine-tuned large language models. In Findings of the… [find](https://scholar.google.com/scholar?q=Haoran+Luo%2C+Haihong+E%2C+Zichen+Tang%2C+Shiyao+Peng%2C+Yikai+Guo%2C+Wentai+Zhang%2C+Chenghao+Ma%2C+Guanting+Dong%2C+Meina+Song%2C+Wei+Lin%2C+Yifan+Zhu%2C+and+Anh+Tuan+Luu%2E+2024a%2E+Chatkbqa%3A+A+generate%2Dthen%2Dretrieve+framew)
22. Linhao Luo, Jiaxin Ju, Bo Xiong, Yuan-Fang Li, Gholamreza Haffari, and Shirui Pan. 2025. Chatrule: Mining logical rules with large language models for knowledge graph reasoning. In Advances in Knowledge Discovery and Data Mining - 29th Pacific-Asia Conference on Knowledge Discovery and Data Mining,… [find](https://scholar.google.com/scholar?q=Linhao+Luo%2C+Jiaxin+Ju%2C+Bo+Xiong%2C+Yuan%2DFang+Li%2C+Gholamreza+Haffari%2C+and+Shirui+Pan%2E+2025%2E+Chatrule%3A+Mining+logical+rules+with+large+language+models+for+knowledge+graph+reasoning%2E+In+Advances+in+Knowled)
23. Linhao Luo, Yuan-Fang Li, Gholamreza Haffari, and Shirui Pan. 2024b. Reasoning on graphs: Faithful and interpretable large language model reasoning. In The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024. OpenReview.net. [find](https://scholar.google.com/scholar?q=Linhao+Luo%2C+Yuan%2DFang+Li%2C+Gholamreza+Haffari%2C+and+Shirui+Pan%2E+2024b%2E+Reasoning+on+graphs%3A+Faithful+and+interpretable+large+language+model+reasoning%2E+In+The+Twelfth+International+Conference+on+Learning)
24. Man Luo, Xin Xu, Zhuyun Dai, Panupong Pasupat, Mehran Kazemi, Chitta Baral, Vaiva Imbrasaite, and Vincent Zhao. 2024c. Dr.icl: Demonstration-retrieved in-context learning. DATA INTELLI-GENCE, 6(4):909–922. [find](https://scholar.google.com/scholar?q=Man+Luo%2C+Xin+Xu%2C+Zhuyun+Dai%2C+Panupong+Pasupat%2C+Mehran+Kazemi%2C+Chitta+Baral%2C+Vaiva+Imbrasaite%2C+and+Vincent+Zhao%2E+2024c%2E+Dr%2Eicl%3A+Demonstration%2Dretrieved+in%2Dcontext+learning%2E+DATA+INTELLI%2DGENCE%2C+6%284%29%3A909)
25. Shengjie Ma, Chengjin Xu, Xuhui Jiang, Muzhi Li, Huaren Qu, Cehao Yang, Jiaxin Mao, and Jian Guo. 2025. Think-on-graph 2.0: Deep and faithful large language model reasoning with knowledge-guided retrieval augmented generation. In The Thirteenth International Conference on Learning Representations, I… [find](https://scholar.google.com/scholar?q=Shengjie+Ma%2C+Chengjin+Xu%2C+Xuhui+Jiang%2C+Muzhi+Li%2C+Huaren+Qu%2C+Cehao+Yang%2C+Jiaxin+Mao%2C+and+Jian+Guo%2E+2025%2E+Think%2Don%2Dgraph+2%2E0%3A+Deep+and+faithful+large+language+model+reasoning+with+knowledge%2Dguided+retri)
26. Shirui Pan, Linhao Luo, Yufei Wang, Chen Chen, Jiapu Wang, and Xindong Wu. 2024. [Unifying large language models and knowledge graphs: A roadmap](https://md.research.elamur.ai/free/en/papers/pan-2023-unifying-large-language-models-knowledge/). IEEE Trans. Knowl. Data Eng., 36(7):3580–3599.
27. Stephen E. Robertson and Hugo Zaragoza. 2009. The probabilistic relevance framework: BM25 and beyond. Found. Trends Inf. Retr., 3(4):333–389. [find](https://scholar.google.com/scholar?q=Stephen+E%2E+Robertson+and+Hugo+Zaragoza%2E+2009%2E+The+probabilistic+relevance+framework%3A+BM25+and+beyond%2E+Found%2E+Trends+Inf%2E+Retr%2E%2C+3%284%29%3A333%E2%80%93389%2E)
28. Apoorv Saxena, Aditay Tripathi, and Partha P. Talukdar. 2020. [Improving multi-hop question answering over knowledge graphs using knowledge base embeddings](https://md.research.elamur.ai/free/en/papers/saxena-2020-improving-multi-hop-question-answering/). In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, July 5-10, 2020, pages 4498–4507.…
29. Priyanka Sen, Armin Oliya, and Amir Saffari. 2021. Expanding end-to-end question answering on differentiable knowledge graphs with intersection. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021, Virtual Event / Punta Cana, Dominican Republic, 7-11… [find](https://scholar.google.com/scholar?q=Priyanka+Sen%2C+Armin+Oliya%2C+and+Amir+Saffari%2E+2021%2E+Expanding+end%2Dto%2Dend+question+answering+on+differentiable+knowledge+graphs+with+intersection%2E+In+Proceedings+of+the+2021+Conference+on+Empirical+Meth)
30. Yiheng Shu, Zhiwei Yu, Yuhan Li, Börje F. Karlsson, Tingting Ma, Yuzhong Qu, and Chin-Yew Lin. 2022. TIARA: multi-grained retrieval for robust question answering over large knowledge bases. CoRR, abs/2210.12925. — [arXiv:2210.12925](https://arxiv.org/abs/2210.12925) [find](https://scholar.google.com/scholar?q=Yiheng+Shu%2C+Zhiwei+Yu%2C+Yuhan+Li%2C+B%C3%B6rje+F%2E+Karlsson%2C+Tingting+Ma%2C+Yuzhong+Qu%2C+and+Chin%2DYew+Lin%2E+2022%2E+TIARA%3A+multi%2Dgrained+retrieval+for+robust+question+answering+over+large+knowledge+bases%2E+CoRR%2C+abs%2F)
31. Jiashuo Sun, Chengjin Xu, Lumingyuan Tang, Saizhuo Wang, Chen Lin, Yeyun Gong, Lionel M. Ni, Heung-Yeung Shum, and Jian Guo. 2024. Think-on-graph: Deep and responsible reasoning of large language model on knowledge graph. In The Twelfth International Conference on Learning Representations, ICLR 2024… [find](https://scholar.google.com/scholar?q=Jiashuo+Sun%2C+Chengjin+Xu%2C+Lumingyuan+Tang%2C+Saizhuo+Wang%2C+Chen+Lin%2C+Yeyun+Gong%2C+Lionel+M%2E+Ni%2C+Heung%2DYeung+Shum%2C+and+Jian+Guo%2E+2024%2E+Think%2Don%2Dgraph%3A+Deep+and+responsible+reasoning+of+large+language+mode)
32. Alon Talmor and Jonathan Berant. 2018. [The web as a knowledge-base for answering complex questions](https://md.research.elamur.ai/free/en/papers/talmor-2018-web-as-knowledge-base-answering/). In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2018, New Orleans, Louisiana, USA, June 1-6…
33. Llama Team. 2024. [The llama 3 herd of models](https://md.research.elamur.ai/free/en/papers/grattafiori-2024-llama-3-herd-models/). CoRR, abs/2407.21783.
34. Yuhang Tian, Dandan Song, Zhijing Wu, Pan Yang, Changzhi Zhou, Jun Yang, Hao Wang, Huipeng Ma, Chenhao Li, and Luan Zhang. 2025. CompKBQA: Component-wise task decomposition for knowledge base question answering. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processin… [find](https://scholar.google.com/scholar?q=Yuhang+Tian%2C+Dandan+Song%2C+Zhijing+Wu%2C+Pan+Yang%2C+Changzhi+Zhou%2C+Jun+Yang%2C+Hao+Wang%2C+Huipeng+Ma%2C+Chenhao+Li%2C+and+Luan+Zhang%2E+2025%2E+CompKBQA%3A+Component%2Dwise+task+decomposition+for+knowledge+base+question)
35. Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton-Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, and 49 others… — [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://md.research.elamur.ai/free/en/papers/touvron-2023-llama-2-open-foundation-fine/)
36. Peng Xiao, Chao Liu, Wei Jia, and Lijun Dong. 2025. Aligned-entities-based fusion embedding on hetero-field knowledge graphs. DATA INTELLIGENCE, 7(3):618–635. [find](https://scholar.google.com/scholar?q=Peng+Xiao%2C+Chao+Liu%2C+Wei+Jia%2C+and+Lijun+Dong%2E+2025%2E+Aligned%2Dentities%2Dbased+fusion+embedding+on+hetero%2Dfield+knowledge+graphs%2E+DATA+INTELLIGENCE%2C+7%283%29%3A618%E2%80%93635%2E)
37. Tianbao Xie, Chen Henry Wu, Peng Shi, Ruiqi Zhong, Torsten Scholak, Michihiro Yasunaga, Chien-Sheng Wu, Ming Zhong, Pengcheng Yin, Sida I. Wang, Victor Zhong, Bailin Wang, Chengzu Li, Connor Boyle, Ansong Ni, Ziyu Yao, Dragomir Radev, Caiming Xiong, Lingpeng Kong, and 4 others. 2022. [Unifiedskg: Uni…](https://md.research.elamur.ai/free/en/papers/xie-2022-unifiedskg-unifying-multi-tasking-structured/)
38. Xi Ye, Semih Yavuz, Kazuma Hashimoto, Yingbo Zhou, and Caiming Xiong. 2022. RNG-KBQA: generation augmented iterative ranking for knowledge base question answering. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2022, Dublin, Ir… [find](https://scholar.google.com/scholar?q=Xi+Ye%2C+Semih+Yavuz%2C+Kazuma+Hashimoto%2C+Yingbo+Zhou%2C+and+Caiming+Xiong%2E+2022%2E+RNG%2DKBQA%3A+generation+augmented+iterative+ranking+for+knowledge+base+question+answering%2E+In+Proceedings+of+the+60th+Annual+Me)
39. Wen-tau Yih, Matthew Richardson, Christopher Meek, Ming-Wei Chang, and Jina Suh. 2016. [The value of semantic parse labeling for knowledge base question answering](https://md.research.elamur.ai/free/en/papers/yih-2016-value-semantic-parse-labeling-knowledge/). In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016, August 7-12, 2016, Berlin, Germany,…
40. Jing Zhang, Xiaokang Zhang, Jifan Yu, Jian Tang, Jie Tang, Cuiping Li, and Hong Chen. 2022. Subgraph retrieval enhanced model for multi-hop knowledge base question answering. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2022,… [find](https://scholar.google.com/scholar?q=Jing+Zhang%2C+Xiaokang+Zhang%2C+Jifan+Yu%2C+Jian+Tang%2C+Jie+Tang%2C+Cuiping+Li%2C+and+Hong+Chen%2E+2022%2E+Subgraph+retrieval+enhanced+model+for+multi%2Dhop+knowledge+base+question+answering%2E+In+Proceedings+of+the+60t)
41. Lingxi Zhang, Jing Zhang, Yanling Wang, Shulin Cao, Xinmei Huang, Cuiping Li, Hong Chen, and Juanzi Li. 2023. FC-KBQA: A fine-to-coarse composition framework for knowledge base question answering. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: L… [find](https://scholar.google.com/scholar?q=Lingxi+Zhang%2C+Jing+Zhang%2C+Yanling+Wang%2C+Shulin+Cao%2C+Xinmei+Huang%2C+Cuiping+Li%2C+Hong+Chen%2C+and+Juanzi+Li%2E+2023%2E+FC%2DKBQA%3A+A+fine%2Dto%2Dcoarse+composition+framework+for+knowledge+base+question+answering%2E+In+)
42. Minghan Zhang, Shu Zhao, Zhen Yang, Hongsheng Wu, Yongxing Lin, Haodong Zou, Jie Chen, and Zhen Duan. 2026. [Thinking beyond the local: Multi-view instructed adaptive reasoning in kg-enhanced llms](https://md.research.elamur.ai/free/en/papers/zhang-2026-thinking-beyond-local-multi-view/). In Findings of the Association for Computational Linguistics: EACL 2026, Rabat, Morocco, March 24-29, 2…
43. Zhiqiang Zhang, Liqiang Wen, and Wen Zhao. 2025. Rule-kbqa: Rule-guided reasoning for complex knowledge base question answering with large language models. In Proceedings of the 31st International Conference on Computational Linguistics, COLING 2025, Abu Dhabi, UAE, January 19-24, 2025, pages 8399–8… [find](https://scholar.google.com/scholar?q=Zhiqiang+Zhang%2C+Liqiang+Wen%2C+and+Wen+Zhao%2E+2025%2E+Rule%2Dkbqa%3A+Rule%2Dguided+reasoning+for+complex+knowledge+base+question+answering+with+large+language+models%2E+In+Proceedings+of+the+31st+International+Con)

*Updated 2 November 2026*