---
title: "Who cites SciKnowEval: Evaluating — 32 citing papers"
description: "32 papers of the Elamur Research library cite «SciKnowEval: Evaluating Multi-level Scientific Knowledge of Large Language Models», found…"
canonical: "https://research.elamur.ai/free/en/papers/feng-2024-sciknoweval-evaluating-multi-level-scientific/cited-by/"
---
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[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [SciKnowEval: Evaluating Multi-level Scientific…](https://md.research.elamur.ai/free/en/papers/feng-2024-sciknoweval-evaluating-multi-level-scientific/) / cited by

# Who cites «SciKnowEval: Evaluating Multi-level Scientific Knowledge of Large Language Models»

32 papers of our library cite it (of the 149,439 whose bibliographies we read) · 7 with a page here, the most cited first · [← back to «SciKnowEval: Evaluating Multi-level Scientific Knowledge of Large Language Models»](https://md.research.elamur.ai/free/en/papers/feng-2024-sciknoweval-evaluating-multi-level-scientific/)

1. **[SciRIFF: A Resource to Enhance Language Model Instruction-Following over Scientific Literature](https://md.research.elamur.ai/free/en/papers/wadden-2025-sciriff-resource-enhance-language-model/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**David Wadden, Kejian Shi, Jacob Morrison, et al.*2025 · 27 citations*
2. **[AI4Research: A Survey of Artificial Intelligence for Scientific Research](https://md.research.elamur.ai/free/en/papers/chen-2025-ai4research-survey-artificial-intelligence/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Qiguang Chen, Mingda Yang, Libo Qin, et al.*2025 · 21 citations*
3. [**ChemAgent: Self-updating Library in Large Language Models Improves Chemical Reasoning arXiv:2501.06590**Xiangru Tang, Tianyu Hu, Muyang Ye, et al.*2025 · 15 citations*](https://md.research.elamur.ai/free/en/papers/tang-2025-chemagent-self-updating-library-large/)
4. **[Instructions are all you need: Self-supervised Reinforcement Learning for Instruction Following](https://md.research.elamur.ai/free/en/papers/ren-2026-instructions-are-all-you-need/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Qingyu Ren, Qianyu He, Powei Chang, et al.*2026 · 3 citations*
5. **[JudgeAgent: Beyond Static Benchmarks for Knowledge-Driven and Dynamic LLM Evaluation](https://md.research.elamur.ai/free/en/papers/shi-2026-judgeagent-beyond-static-benchmarks-knowledge/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhichao Shi, Xuhui Jiang, Chengjin Xu, et al.*2026*
6. **[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*
7. **[WildSci: Advancing Scientific Reasoning from In-the-Wild Literature](https://md.research.elamur.ai/free/en/papers/liu-2026-wildsci-advancing-scientific-reasoning-wild/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Tengxiao Liu, Deepak Nathani, Zekun Li, et al.*2026*

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

- [← back to](https://md.research.elamur.ai/free/en/papers/feng-2024-sciknoweval-evaluating-multi-level-scientific/) — SciKnowEval: Evaluating Multi-level Scientific Knowledge of Large Language Models