--- title: "Who cites Exploring Concept Depth: How — 32 citing papers" description: "32 papers of the Elamur Research library cite «Exploring Concept Depth: How Large Language Models Acquire Knowledge and Concept at…" canonical: "https://research.elamur.ai/free/en/papers/jin-2025-exploring-concept-depth-how-large/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/jin-2025-exploring-concept-depth-how-large/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Exploring Concept Depth: How Large Language…](https://md.research.elamur.ai/free/en/papers/jin-2025-exploring-concept-depth-how-large/) / cited by # Who cites «Exploring Concept Depth: How Large Language Models Acquire Knowledge and Concept at Different Layers?» 32 papers of our library cite it (of the 149,439 whose bibliographies we read) · 13 with a page here, the most cited first · [← back to «Exploring Concept Depth: How Large Language Models Acquire Knowledge and Concept at Different Layers?»](https://md.research.elamur.ai/free/en/papers/jin-2025-exploring-concept-depth-how-large/) 1. **[Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models](https://md.research.elamur.ai/free/en/papers/chen-2025-towards-reasoning-era-survey-long/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Qiguang Chen, Libo Qin, Jinhao Liu, et al.*2025 · 176 citations* 2. [**Token-Budget-Aware LLM Reasoning arXiv:2412.18547**Tingxu Han, Zhenting Wang, Chunrong Fang, et al.*2024 · 136 citations*](https://md.research.elamur.ai/free/en/papers/han-2024-token-budget-aware-llm-reasoning/) 3. **[Disentangling Memory and Reasoning Ability in Large Language Models](https://md.research.elamur.ai/free/en/papers/jin-2025-disentangling-memory-reasoning-ability-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mingyu Jin, Weidi Luo, Sitao Cheng, et al.*2025 · 45 citations* 4. **[AGrail: A Lifelong Agent Guardrail with Effective and Adaptive Safety Detection](https://md.research.elamur.ai/free/en/papers/luo-2025-agrail-lifelong-agent-guardrail-effective/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Weidi Luo, Shenghong Dai, Xiaogeng Liu, et al.*2025 · 20 citations* 5. **[NeuReasoner: Towards Explainable, Controllable, and Unified Reasoning via Mixture-of-Neurons](https://md.research.elamur.ai/free/en/papers/dong-2026-neureasoner-explainable-controllable-unified-reasoning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Haonan Dong, Kehan Jiang, Haoran Ye, et al.*2026 · 13 citations* 6. **[Token Prepending: A Training-Free Approach for Eliciting Better Sentence Embeddings from LLMs](https://md.research.elamur.ai/free/en/papers/fu-2025-token-prepending-training-free-approach/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuchen Fu, Zifeng Cheng, Zhiwei Jiang, et al.*2025 · 10 citations* 7. **[Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2026-locate-steer-improve-practical-survey/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hengyuan Zhang, Zhihao Zhang, Ercong Nie, et al.*2026 · 9 citations* 8. **[GMSA: Enhancing Context Compression via Group Merging and Layer Semantic Alignment](https://md.research.elamur.ai/free/en/papers/tang-2026-gmsa-enhancing-context-compression-group/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jiwei Tang, Zhicheng Zhang, Shunlong Wu, et al.*2026 · 8 citations* 9. **[GraphLoRA: Structure-Aware Low-Rank Adaptation for Large Language Model Recommendation](https://md.research.elamur.ai/free/en/papers/mu-2026-graphlora-structure-aware-low-rank/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Lin Mu, Guoji Wang, Li Ni, et al.*2026* 10. **[How Do LLMs Generate Contrastive Sentiments? A Mechanistic Perspective](https://md.research.elamur.ai/free/en/papers/nguyen-2026-how-do-llms-generate-contrastive/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Van Bach Nguyen, Jörg Schlötterer, Christin Seifert*2026* 11. **[How Retrieved Context Shapes Internal Representations in RAG](https://md.research.elamur.ai/free/en/papers/yeh-2026-how-retrieved-context-shapes-internal/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Samuel Yeh, Sharon Li*2026* 12. **[Layer-aware Dual-directional Modulation for Low-resource Machine Translation](https://md.research.elamur.ai/free/en/papers/zhang-2026-layer-aware-dual-directional-modulation/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Siqi Zhang, Ran Song, Shuting Jiang, et al.*2026* 13. **[Learning Uncertainty from Sequential Internal Dispersion in Large Language Models](https://md.research.elamur.ai/free/en/papers/srey-2026-learning-uncertainty-sequential-internal-dispersion/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ponhvoan Srey, Xiaobao Wu, Cong-Duy T Nguyen, et al.*2026* 19 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/jin-2025-exploring-concept-depth-how-large/) — Exploring Concept Depth: How Large Language Models Acquire Knowledge and Concept at Different Layers?