---
title: "Who cites Large Language Models are — 38 citing papers"
description: "38 papers of the Elamur Research library cite «Large Language Models are In-Context Semantic Reasoners rather than Symbolic…"
canonical: "https://research.elamur.ai/free/en/papers/tang-2023-large-language-models-context-semantic/cited-by/"
---
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[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Large Language Models are In-Context Semantic…](https://md.research.elamur.ai/free/en/papers/tang-2023-large-language-models-context-semantic/) / cited by

# Who cites «Large Language Models are In-Context Semantic Reasoners rather than Symbolic Reasoners»

38 papers of our library cite it (of the 149,439 whose bibliographies we read) · 6 with a page here, the most cited first · [← back to «Large Language Models are In-Context Semantic Reasoners rather than Symbolic Reasoners»](https://md.research.elamur.ai/free/en/papers/tang-2023-large-language-models-context-semantic/)

1. **[The Impact of Reasoning Step Length on Large Language Models](https://md.research.elamur.ai/free/en/papers/jin-2024-impact-reasoning-step-length-large/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Mingyu Jin, Qinkai Yu, Dong Shu, et al.*2024 · 119 citations*
2. **[ChatGPT Is a Knowledgeable but Inexperienced Solver: An Investigation of Commonsense Problem in Large Language Models](https://md.research.elamur.ai/free/en/papers/bian-2024-chatgpt-is-knowledgeable-but-inexperienced/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Ning Bian, Xianpei Han, Le Sun, et al.*2024 · 106 citations*
3. [**BHASA: A Holistic Southeast Asian Linguistic and Cultural Evaluation Suite for Large Language Models arXiv:2309.06085**Wei Qi Leong, Jian Gang Ngui, Yosephine Susanto, et al.*2023 · 33 citations*](https://md.research.elamur.ai/free/en/papers/leong-2023-bhasa-holistic-southeast-asian-linguistic/)
4. **[Is Chain-of-Thought Reasoning of LLMs a Mirage? A Data Distribution Lens](https://md.research.elamur.ai/free/en/papers/zhao-2026-is-chain-thought-reasoning-llms/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Chengshuai Zhao, Zhen Tan, Pingchuan Ma, et al.*2026 · 21 citations*
5. **[RethinkMCTS: Refining Erroneous Thoughts in Monte Carlo Tree Search for Code Generation](https://md.research.elamur.ai/free/en/papers/li-2025-rethinkmcts-refining-erroneous-thoughts-monte/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Qingyao Li, Wei Xia, Xinyi Dai, et al.*2025 · 16 citations*
6. **[Shuttle Between Symbolic Instructions and Neural Parameters of Large Language Models](https://md.research.elamur.ai/free/en/papers/sun-2026-shuttle-between-symbolic-instructions-neural/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Wangtao Sun, Haotian Xu, Huanxuan Liao, et al.*2026*

32 more papers of our library cite it; they have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/papers/tang-2023-large-language-models-context-semantic/) — Large Language Models are In-Context Semantic Reasoners rather than Symbolic Reasoners