--- title: "Who cites ChatQA: Surpassing GPT-4 on — 48 citing papers" description: "48 papers of the Elamur Research library cite «ChatQA: Surpassing GPT-4 on Conversational QA and RAG», found in their bibliographies…" canonical: "https://research.elamur.ai/free/en/papers/liu-2024-chatqa-surpassing-gpt-4-conversational/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/liu-2024-chatqa-surpassing-gpt-4-conversational/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [ChatQA: Surpassing GPT-4 on Conversational QA and…](https://md.research.elamur.ai/free/en/papers/liu-2024-chatqa-surpassing-gpt-4-conversational/) / cited by # Who cites «ChatQA: Surpassing GPT-4 on Conversational QA and RAG» 48 papers of our library cite it (of the 149,439 whose bibliographies we read) · 13 with a page here, the most cited first · [← back to «ChatQA: Surpassing GPT-4 on Conversational QA and RAG»](https://md.research.elamur.ai/free/en/papers/liu-2024-chatqa-surpassing-gpt-4-conversational/) 1. [**NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models arXiv:2405.17428**Chankyu Lee, Rajarshi Roy, Mengyao Xu, et al.*2024 · 222 citations*](https://md.research.elamur.ai/free/en/papers/lee-2024-nv-embed-improved-techniques-training/) 2. **[Searching for Best Practices in Retrieval-Augmented Generation](https://md.research.elamur.ai/free/en/papers/wang-2024-searching-best-practices-retrieval-augmented/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Xiaohua Wang, Zhenghua Wang, Xuan Gao, et al.*2024 · 150 citations* 3. [**RAFT: Adapting Language Model to Domain Specific RAG arXiv:2403.10131**Tianjun Zhang, Shishir G. Patil, Naman Jain, et al.*2024 · 137 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2024-raft-adapting-language-model-domain/) 4. [**Nemotron-4 340B Technical Report arXiv:2406.11704**Nvidia: Bo Adler, Niket Agarwal, Ashwath Aithal, et al.*2024 · 107 citations*](https://md.research.elamur.ai/free/en/papers/adler-2024-nemotron-4-340b-technical-report/) 5. **[FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation](https://md.research.elamur.ai/free/en/papers/zhang-2025-faithfulrag-fact-level-conflict-modeling/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Qinggang Zhang, Zhishang Xiang, Yilin Xiao, et al.*2025 · 27 citations* 6. **[RAG-Instruct: Boosting LLMs with Diverse Retrieval-Augmented Instructions](https://md.research.elamur.ai/free/en/papers/liu-2025-rag-instruct-boosting-llms-diverse/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Wanlong Liu, Junying Chen, Ke Ji, et al.*2025 · 19 citations* 7. **[MIRAGE-Bench: Automatic Multilingual Benchmark Arena for Retrieval-Augmented Generation Systems](https://md.research.elamur.ai/free/en/papers/thakur-2025-mirage-bench-automatic-multilingual-benchmark/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Nandan Thakur, Suleman Kazi, Ge Luo, et al.*2025 · 15 citations* 8. **[What are the Essential Factors in Crafting Effective Long Context Multi-Hop Instruction Datasets? Insights and Best Practices](https://md.research.elamur.ai/free/en/papers/chen-2025-what-are-essential-factors-crafting/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhi Chen, Qiguang Chen, Libo Qin, et al.*2025 · 15 citations* 9. [**MA-RAG: Multi-Agent Retrieval-Augmented Generation via Collaborative Chain-of-Thought Reasoning arXiv:2505.20096**Thang Nguyen, Peter Chin, Yu-Wing Tai*2025 · 14 citations*](https://md.research.elamur.ai/free/en/papers/nguyen-2025-ma-rag-multi-agent-retrieval/) 10. **[ChatR1: Reinforcement Learning for Conversational Reasoning and Retrieval Augmented Question Answering](https://md.research.elamur.ai/free/en/papers/lupart-2026-chatr1-reinforcement-learning-conversational-reasoning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Simon Lupart, Mohammad Aliannejadi, Evangelos Kanoulas*2026 · 5 citations* 11. **[Agentic Conversational Search with Contextualized Reasoning via Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/mo-2026-agentic-conversational-search-contextualized-reasoning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Fengran Mo, Yifan Gao, Sha Li, et al.*2026 · 2 citations* 12. **[5ting at SemEval-2026 Task 8: Strong End-to-End Multi-Turn RAG via LLM-Based Reranking and Faithfulness Control](https://md.research.elamur.ai/free/en/papers/nguyen-2026-5ting-semeval-2026-task-8/) [International Workshop on Semantic Evaluation (SemEval)](https://md.research.elamur.ai/free/en/venues/semeval/)**Thien-Qua T-Nguyen, Chi Hoang, Nguyen Tran, et al.*2026 · 1 citation* 13. **[MTR-Suite: A Framework for Evaluating and Synthesizing Conversational Retrieval Benchmarks](https://md.research.elamur.ai/free/en/papers/ruan-2026-mtr-suite-framework-evaluating-synthesizing/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Junhao Ruan, Abudukeyumu Abudula, Bei Li, et al.*2026* 35 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/liu-2024-chatqa-surpassing-gpt-4-conversational/) — ChatQA: Surpassing GPT-4 on Conversational QA and RAG