---
title: "Who cites Yifan Yang — 58 papers of our library"
description: "58 papers of the Elamur Research library cite the works of Yifan Yang, found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/yifan-yang/cited-by/"
---
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# Who cites Yifan Yang

58 citations of their works from 58 papers of our library (of the 149,439 whose bibliographies we read) · 26 with a page here, the most cited first · [← Yifan Yang](https://md.research.elamur.ai/free/en/authors/yifan-yang/)

1. [**ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs arXiv:2307.16789**Yujia Qin, Shihao Liang, Yining Ye, et al.*2023 · 589 citations*](https://md.research.elamur.ai/free/en/papers/qin-2023-toolllm-facilitating-large-language-models/)
2. [**Retrieval-Augmented Generation for AI-Generated Content: A Survey arXiv:2402.19473**Penghao Zhao, Hailin Zhang, Qinhan Yu, et al.*2024 · 301 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2024-retrieval-augmented-generation-ai-generated/)
3. **[MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoning](https://md.research.elamur.ai/free/en/papers/tang-2024-medagents-large-language-models-as/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xiangru Tang, Anni Zou, Zhuosheng Zhang, et al.*2024 · 246 citations*
4. **[Tool Learning with Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/qu-2024-tool-learning-large-language-models/) [Frontiers of Computer Science](https://md.research.elamur.ai/free/en/venues/frontiers-of-computer-science/)**Changle Qu, Sunhao Dai, Xiaochi Wei, et al.*2024 · 195 citations*
5. [**Evaluating Large Language Models: A Comprehensive Survey arXiv:2310.19736**Zishan Guo, Renren Jin, Chuang Liu, et al.*2023 · 182 citations*](https://md.research.elamur.ai/free/en/papers/guo-2023-evaluating-large-language-models-comprehensive/)
6. **[ToolQA: A Dataset for LLM Question Answering with External Tools](https://md.research.elamur.ai/free/en/papers/zhuang-2023-toolqa-dataset-llm-question-answering/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yuchen Zhuang, Yue Yu, Kuan Wang, et al.*2023 · 182 citations*
7. [**Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity arXiv:2310.07521**Cunxiang Wang, Xiaoze Liu, Yuanhao Yue, et al.*2023 · 171 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-survey-factuality-large-language-models/)
8. [**Capabilities of Gemini Models in Medicine arXiv:2404.18416**Khaled Saab, Tao Tu, Wei-Hung Weng, et al.*2024 · 170 citations*](https://md.research.elamur.ai/free/en/papers/saab-2024-capabilities-gemini-models-medicine/)
9. **[ToolkenGPT: Augmenting Frozen Language Models with Massive Tools via Tool Embeddings](https://md.research.elamur.ai/free/en/papers/hao-2023-toolkengpt-augmenting-frozen-language-models/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Shibo Hao, Tianyang Liu, Zhen Wang, et al.*2023 · 147 citations*
10. [**BioMedLM: A 2.7B Parameter Language Model Trained On Biomedical Text arXiv:2403.18421**Elliot Bolton, Abhinav Venigalla, Michihiro Yasunaga, et al.*2024 · 79 citations*](https://md.research.elamur.ai/free/en/papers/bolton-2024-biomedlm-2-7b-parameter-language/)
11. [**Evaluation of OpenAI o1: Opportunities and Challenges of AGI arXiv:2409.18486**Tianyang Zhong, Zhengliang Liu, Yi Pan, et al.*2024 · 64 citations*](https://md.research.elamur.ai/free/en/papers/zhong-2024-evaluation-openai-o1-opportunities-challenges/)
12. **[Self-Distillation Bridges Distribution Gap in Language Model Fine-Tuning](https://md.research.elamur.ai/free/en/papers/yang-2024-self-distillation-bridges-distribution-gap/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhaorui Yang, Tianyu Pang, Haozhe Feng, et al.*2024 · 63 citations*
13. [**LAB-Bench: Measuring Capabilities of Language Models for Biology Research arXiv:2407.10362**Jon M. Laurent, Joseph D. Janizek, Michael Ruzo, et al.*2024 · 62 citations*](https://md.research.elamur.ai/free/en/papers/laurent-2024-lab-bench-measuring-capabilities/)
14. **[SciAgent: Tool-augmented Language Models for Scientific Reasoning](https://md.research.elamur.ai/free/en/papers/ma-2024-sciagent-tool-augmented-language-models/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yubo Ma, Zhibin Gou, Junheng Hao, et al.*2024 · 55 citations*
15. **[ToolEyes: Fine-Grained Evaluation for Tool Learning Capabilities of Large Language Models in Real-world Scenarios](https://md.research.elamur.ai/free/en/papers/ye-2025-tooleyes-fine-grained-evaluation-tool/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Junjie Ye, Guanyu Li, SongYang Gao, et al.*2025 · 35 citations*
16. **[Alignment for Efficient Tool Calling of Large Language Models](https://md.research.elamur.ai/free/en/papers/xu-2025-alignment-efficient-tool-calling-large/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Hongshen Xu, Zihan Wang, Zichen Zhu, et al.*2025 · 13 citations*
17. **[Efficient Tool Use with Chain-of-Abstraction Reasoning](https://md.research.elamur.ai/free/en/papers/gao-2025-efficient-tool-use-chain-abstraction/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Silin Gao, Jane Dwivedi-Yu, Ping Yu, et al.*2025 · 13 citations*
18. [**BioKGBench: A Knowledge Graph Checking Benchmark of AI Agent for Biomedical Science arXiv:2407.00466**Xinna Lin, Siqi Ma, Junjie Shan, et al.*2024 · 12 citations*](https://md.research.elamur.ai/free/en/papers/lin-2024-biokgbench-knowledge-graph-checking-benchmark/)
19. **[PERM: Psychology-grounded Empathetic Reward Modeling for Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2026-perm-psychology-grounded-empathetic-reward/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Chengbing Wang, Wuqiang Zheng, Yang Zhang, et al.*2026 · 8 citations*
20. **[Too Good to be Bad: On the Failure of LLMs to Role-Play Villains](https://md.research.elamur.ai/free/en/papers/yi-2026-too-good-be-bad-failure/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zihao Yi, Qingxuan Jiang, Ruotian Ma, et al.*2026 · 3 citations*
21. **[AEQ-Bench: Measuring Empathy of Omni-Modal Large Models](https://md.research.elamur.ai/free/en/papers/luo-2026-aeq-bench-measuring-empathy-omni/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xuan Luo, Lewei Yao, Lanqing Hong, et al.*2026*
22. **[KGRxn-LLM: Knowledge Graph Enhanced Large Language Models for Molecular Reaction Reasoning](https://md.research.elamur.ai/free/en/papers/liu-2026-kgrxn-llm-knowledge-graph-enhanced/) [BioNLP](https://md.research.elamur.ai/free/en/venues/bionlp/)**Weichen Liu, Qiyao Xue, Yuyang Wu, et al.*2026*
23. **[LLM-Based Multi-Agent Systems for Clinical Workflows: A Survey of AI Hospitals](https://md.research.elamur.ai/free/en/papers/yao-2026-llm-based-multi-agent-systems/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zonghai Yao, Hong yu*2026*
24. **[Towards Fine-Grained and Multi-Granular Contrastive Language-Speech Pre-training](https://md.research.elamur.ai/free/en/papers/yang-2026-fine-grained-multi-granular-contrastive/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yifan Yang, Bing Han, Hui Wang, et al.*2026*
25. **[UniSRM: A Unified Speech Reward Model for Reasoning-Based Fine-grained Assessment](https://md.research.elamur.ai/free/en/papers/wang-2026-unisrm-unified-speech-reward-model/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuanyuan Wang, Dongchao Yang, Yayue Deng, et al.*2026*
26. [**Survey of Tool Use in Large Language Models (大语言模型的工具使用综述) Acta Automatica Sinica (自动化学报)**Yining Zheng (郑逸宁), Zhen Yu (余镇), Bufan Li (李不凡), et al.*2025*](https://md.research.elamur.ai/free/en/papers/zheng-2025-tool-use-large/)

32 more citing papers of our library have no page here yet.

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