---
title: "Who cites A Peek into Token Bias: Large — 31 citing papers"
description: "31 papers of the Elamur Research library cite «A Peek into Token Bias: Large Language Models Are Not Yet Genuine Reasoners», found in their…"
canonical: "https://research.elamur.ai/free/en/papers/jiang-2024-peek-into-token-bias-large/cited-by/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/jiang-2024-peek-into-token-bias-large/cited-by/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [A Peek into Token Bias: Large Language Models Are…](https://md.research.elamur.ai/free/en/papers/jiang-2024-peek-into-token-bias-large/) / cited by

# Who cites «A Peek into Token Bias: Large Language Models Are Not Yet Genuine Reasoners»

31 papers of our library cite it (of the 149,439 whose bibliographies we read) · 8 with a page here, the most cited first · [← back to «A Peek into Token Bias: Large Language Models Are Not Yet Genuine Reasoners»](https://md.research.elamur.ai/free/en/papers/jiang-2024-peek-into-token-bias-large/)

1. [**GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models arXiv:2410.05229**Iman Mirzadeh, Keivan Alizadeh, Hooman Shahrokhi, et al.*2024 · 273 citations*](https://md.research.elamur.ai/free/en/papers/mirzadeh-2024-gsm-symbolic-understanding-limitations-mathematical/)
2. [**LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods arXiv:2412.05579**Haitao Li, Qian Dong, Junjie Chen, et al.*2024 · 195 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-llms-judges-comprehensive-survey-llm/)
3. **[On Memorization of Large Language Models in Logical Reasoning](https://md.research.elamur.ai/free/en/papers/xie-2025-memorization-large-language-models-logical/) [International Joint Conference on Natural Language Processing (IJCNLP)](https://md.research.elamur.ai/free/en/venues/ijcnlp/)**Chulin Xie, Yangsibo Huang, Chiyuan Zhang, et al.*2025 · 45 citations*
4. **[Inner Thinking Transformer: Leveraging Dynamic Depth Scaling to Foster Adaptive Internal Thinking](https://md.research.elamur.ai/free/en/papers/chen-2025-inner-thinking-transformer-leveraging-dynamic/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yilong Chen, Junyuan Shang, Zhenyu Zhang, et al.*2025 · 16 citations*
5. **[FACT-E: Causality-Inspired Evaluation for Trustworthy Chain-of-Thought Reasoning](https://md.research.elamur.ai/free/en/papers/sun-2026-fact-e-causality-inspired-evaluation/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yuxi Sun, Aoqi Zuo, Haotian Xie, et al.*2026 · 7 citations*
6. **[Garbage In, Reasoning Out? Why Benchmark Scores are Unreliable and What to Do About It](https://md.research.elamur.ai/free/en/papers/mousavi-2026-garbage-reasoning-out-why-benchmark/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Seyed Mahed Mousavi, Edoardo Cecchinato, Lucia Horníková, et al.*2026 · 2 citations*
7. **[CRiT-QA: Evaluating Multi-hop Reasoning with Counterfactual Chains and Distractor Traps](https://md.research.elamur.ai/free/en/papers/yun-2026-crit-qa-evaluating-multi-hop/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Jungmin Yun, June Hyoung Kwon, Youngbin Kim*2026*
8. **[GSM-Noise: Exploring and Enhancing Large Language Models' Reasoning under Noisy Inputs](https://md.research.elamur.ai/free/en/papers/zhang-2026-gsm-noise-exploring-enhancing-large/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhengxin Zhang, Chengyu Huang, Xufu Liu, et al.*2026*

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

- [← back to](https://md.research.elamur.ai/free/en/papers/jiang-2024-peek-into-token-bias-large/) — A Peek into Token Bias: Large Language Models Are Not Yet Genuine Reasoners