--- title: "Who cites Leveraging Pre-trained Large — 63 citing papers" description: "63 papers of the Elamur Research library cite «Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for…" canonical: "https://research.elamur.ai/free/en/papers/guan-2023-leveraging-pre-trained-large-language/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/guan-2023-leveraging-pre-trained-large-language/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Leveraging Pre-trained Large Language Models to…](https://md.research.elamur.ai/free/en/papers/guan-2023-leveraging-pre-trained-large-language/) / cited by # Who cites «Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning» 63 papers of our library cite it (of the 149,439 whose bibliographies we read) · 16 with a page here, the most cited first · [← back to «Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning»](https://md.research.elamur.ai/free/en/papers/guan-2023-leveraging-pre-trained-large-language/) 1. [**Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context arXiv:2403.05530**Gemini Team, Petko Georgiev, Ving Ian Lei, et al.*2024 · 1,589 citations*](https://md.research.elamur.ai/free/en/papers/gemini-team-2024-gemini-1-5-unlocking-multimodal/) 2. **[PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Change](https://md.research.elamur.ai/free/en/papers/valmeekam-2022-planbench-extensible-benchmark-evaluating-large/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Karthik Valmeekam, Matthew Marquez, Alberto Olmo, et al.*2022 · 222 citations* 3. [**LLM+P: Empowering Large Language Models with Optimal Planning Proficiency arXiv:2304.11477**Bo Liu, Yuqian Jiang, Xiaohan Zhang, et al.*2023 · 216 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-llm-p-empowering-large-language/) 4. [**Understanding the planning of LLM agents: A survey arXiv:2402.02716**Xu Huang, Weiwen Liu, Xiaolong Chen, et al.*2024 · 128 citations*](https://md.research.elamur.ai/free/en/papers/huang-2024-understanding-planning-llm-agents-survey/) 5. [**LLMs Can't Plan, But Can Help Planning in LLM-Modulo Frameworks arXiv:2402.01817 · Proceedings of the 41 st International Conference on Machine Learning, Vienna, Austria. PMLR 235, 2024**Subbarao Kambhampati, Karthik Valmeekam, Lin Guan, et al.*2024 · 88 citations*](https://md.research.elamur.ai/free/en/papers/kambhampati-2024-llms-t-plan-but-help/) 6. **[PlanGenLLMs: A Modern Survey of LLM Planning Capabilities](https://md.research.elamur.ai/free/en/papers/wei-2025-plangenllms-modern-survey-llm-planning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hui Wei, Zihao Zhang, Shenghua He, et al.*2025 · 55 citations* 7. [**Q*: Improving Multi-step Reasoning for LLMs with Deliberative Planning arXiv:2406.14283**Chaojie Wang, Yanchen Deng, Zhiyi Lyu, et al.*2024 · 53 citations*](https://md.research.elamur.ai/free/en/papers/wang-2024-q-improving-multi-step-reasoning/) 8. [**Towards LogiGLUE: A Brief Survey and A Benchmark for Analyzing Logical Reasoning Capabilities of Language Models arXiv:2310.00836**Man Luo, Shrinidhi Kumbhar, Ming shen, et al.*2023 · 27 citations*](https://md.research.elamur.ai/free/en/papers/luo-2023-towards-logiglue-brief-survey-benchmark/) 9. [**PlaSma: Making Small Language Models Better Procedural Knowledge Models for (Counterfactual) Planning arXiv:2305.19472**Faeze Brahman, Chandra Bhagavatula, Valentina Pyatkin, et al.*2023 · 25 citations*](https://md.research.elamur.ai/free/en/papers/brahman-2023-plasma-making-small-language-models/) 10. **[Large Language Models Can Solve Real-World Planning Rigorously with Formal Verification Tools](https://md.research.elamur.ai/free/en/papers/hao-2025-large-language-models-can-solve/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Yilun Hao, Yongchao Chen, Yang Zhang, et al.*2025 · 18 citations* 11. **[Planning with Multi-Constraints via Collaborative Language Agents](https://md.research.elamur.ai/free/en/papers/zhang-2025-planning-multi-constraints-collaborative-language/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Cong Zhang, Xin Deik Goh, Dexun Li, et al.*2025 · 10 citations* 12. **[SEED: Accelerating Reasoning Tree Construction via Scheduled Speculative Decoding](https://md.research.elamur.ai/free/en/papers/wang-2025-seed-accelerating-reasoning-tree-construction/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Zhenglin Wang, Jialong Wu, Yilong Lai, et al.*2025 · 8 citations* 13. **[Process Reward Models Meet Planning: Generating Precise and Scalable Datasets for Step-Level Rewards](https://md.research.elamur.ai/free/en/papers/pisano-2026-process-reward-models-meet-planning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Raffaele Pisano, Roberto Navigli*2026 · 1 citation* 14. **[GaLa: Hypergraph-Guided Visual Language Models for Procedural Planning](https://md.research.elamur.ai/free/en/papers/wang-2026-gala-hypergraph-guided-visual-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Kun Wang, Li Yiming, Mingcheng Qu, et al.*2026* 15. **[Knowledge Vector of Logical Reasoning in Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2026-knowledge-vector-logical-reasoning-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zixuan Wang, Yuanyuan Lei*2026* 16. **[To Skip, to Swap or to Not Swap? Identifying Step Transition Types in Instructional Manuals](https://md.research.elamur.ai/free/en/papers/yang-2026-skip-swap-or-not-swap/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Hsiu-Yu Yang, Michael Roth, Andreas Bulling, et al.*2026* 47 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/guan-2023-leveraging-pre-trained-large-language/) — Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning