Who cites «RAG-Instruct: Boosting LLMs with Diverse Retrieval-Augmented Instructions»
- How Is LLM Reasoning Distracted by Irrelevant Context? An Analysis Using a Controlled BenchmarkMinglai Yang, Ethan Huang, Liang Zhang, et al.2025 · 25 citations
- UORA: Uniform Orthogonal Reinitialization Adaptation in Parameter Efficient Fine-Tuning of Large ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Xueyan Zhang, Jinman Zhao, Zhifei Yang, et al.2025 · 14 citations
- REARANK: Reasoning Re-ranking Agent via Reinforcement LearningConference on Empirical Methods in Natural Language Processing (EMNLP)Le Zhang, Bo Wang, Xipeng Qiu, et al.2025 · 12 citations
- Tiny Budgets, Big Gains: Parameter Placement Strategy in Parameter Super-Efficient Fine-TuningConference on Empirical Methods in Natural Language Processing (EMNLP)Jinman Zhao, Xueyan Zhang, Jiaru Li, et al.2025 · 8 citations
- FinCall-Surprise: A Large Scale Multi-modal Benchmark for Earning Surprise PredictionAnnual Meeting of the Association for Computational Linguistics (ACL)Dong Shu, Yanguang Liu, Huopu Zhang, et al.2026 · 1 citation
This page in Markdown → https://md.research.elamur.ai/free/en/papers/liu-2025-rag-instruct-boosting-llms-diverse/cited-by/