Elamur Research⌕
☰
home / papers / INSTRUCTEVAL: Towards Holistic Evaluation of… / cited by

Who cites «INSTRUCTEVAL: Towards Holistic Evaluation of Instruction-Tuned Large Language Models»

63 papers of our library cite it (of the 141,840 whose bibliographies we read) · 13 with a page here, the most cited first · ← back to «INSTRUCTEVAL: Towards Holistic Evaluation of Instruction-Tuned Large Language Models»

  1. TinyLlama: An Open-Source Small Language ModelarXiv:2401.02385Peiyuan Zhang, Guangtao Zeng, Tianduo Wang, et al.2024 · 351 citations
  2. Prometheus: Inducing Fine-grained Evaluation Capability in Language ModelsarXiv:2310.08491Seungone Kim, Jamin Shin, Yejin Cho, et al.2023 · 267 citations
  3. LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation DatasetarXiv:2309.11998Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, et al.2023 · 236 citations
  4. AlpaGasus: Training A Better Alpaca with Fewer DataarXiv:2307.08701Lichang Chen, Shiyang Li, Jun Yan, et al.2023 · 215 citations
  5. Orca 2: Teaching Small Language Models How to ReasonarXiv:2311.11045Arindam Mitra, Luciano Del Corro, Shweti Mahajan, et al.2023 · 160 citations
  6. FLASK: Fine-grained Language Model Evaluation based on Alignment Skill SetsarXiv:2307.10928Seonghyeon Ye, Doyoung Kim, Sungdong Kim, et al.2023 · 94 citations
  7. Machine Translation Meta Evaluation through Translation Accuracy Challenge SetsComputational LinguisticsNikita Moghe, Arnisa Fazla, Chantal Amrhein, et al.2025 · 28 citations
  8. Do Large Language Models Understand Word Senses?Domenico Meconi, Simone Stirpe, Federico Martelli2025 · 18 citations

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

This page in Markdown → https://md.research.elamur.ai/free/en/papers/chia-2023-instructeval-towards-holistic-evaluation-instruction/cited-by/