{
"count": 5,
"items": [
{
"title": "Corrective Retrieval Augmented Generation",
"authors": [
"Shi-Qi Yan",
"Jia-Chen Gu",
"Yun Zhu",
"Zhen-Hua Ling"
],
"year": 2026,
"date": "2026-01-01",
"type": "conference-paper",
"doi": "10.18653/2026.findings-aacl.151",
"doiUrl": "https://doi.org/10.18653/2026.findings-aacl.151",
"citedBy": 717,
"venue": null,
"isOpenAccess": true,
"pdfUrl": "https://aclanthology.org/2026.findings-aacl.151.pdf",
"landingUrl": "https://doi.org/10.18653/2026.findings-aacl.151",
"abstract": "Large language models (LLMs) inevitably exhibit hallucinations since the accuracy of generated texts cannot be secured solely by the parametric knowledge they encapsulate.Although",
"openalexId": "https://openalex.org/W4410705984",
"source": "openalex",
"query": "retrieval augmented generation",
"scrapedAt": "2026-11-12T22:57:23Z"
},
{
"title": "Graph Retrieval-Augmented Generation: A Survey",
"authors": [
"Boci Peng",
"Yun Zhu",
"Yongchao Liu",
"Xiaohe Bo",
"Haizhou Shi",
"Chuntao Hong",
"Yan Zhang",
"Siliang Tang"
],
"year": 2025,
"date": "2025-11-19",
"type": "article",
"doi": "10.1145/3777378",
"doiUrl": "https://doi.org/10.1145/3777378",
"citedBy": 715,
"venue": "ACM Transactions on Information Systems",
"isOpenAccess": true,
"pdfUrl": "https://arxiv.org/pdf/2408.08921",
"landingUrl": "https://doi.org/10.1145/3777378",
"abstract": "Recently, Retrieval-Augmented Generation (RAG) has achieved remarkable success in addressing the challenges of Large Language Models (LLMs) without necessitating retraining. By ref",
"openalexId": "https://openalex.org/W4416379250",
"source": "openalex",
"query": "retrieval augmented generation",
"scrapedAt": "2026-11-12T22:57:24Z"
},
{
"title": "Active Retrieval Augmented Generation",
"authors": [
"Zhengbao Jiang",
"Frank F. Xu",
"Luyu Gao",
"Zhiqing Sun",
"Qian Liu",
"Jane Dwivedi-Yu",
"Yiming Yang",
"Jamie Callan",
"Graham Neubig"
],
"year": 2023,
"date": "2023-01-01",
"type": "conference-paper",
"doi": "10.18653/v1/2023.emnlp-main.495",
"doiUrl": "https://doi.org/10.18653/v1/2023.emnlp-main.495",
"citedBy": 1043,
"venue": "Conference on Empirical Methods in Natural Language Processing (EMNLP)",
"isOpenAccess": true,
"pdfUrl": "https://aclanthology.org/2023.emnlp-main.495.pdf",
"landingUrl": "https://doi.org/10.18653/v1/2023.emnlp-main.495",
"abstract": "Zhengbao Jiang, Frank Xu, Luyu Gao, Zhiqing Sun, Qian Liu, Jane Dwivedi-Yu, Yiming Yang, Jamie Callan, Graham Neubig. Proceedings of the 2023 Conference on Empirical Methods in Nat",
"openalexId": "https://openalex.org/W4389519118",
"source": "openalex",
"query": "retrieval augmented generation",
"scrapedAt": "2026-11-12T22:57:24Z"
},
{
"title": "Evaluation of Retrieval-Augmented Generation: A Survey",
"authors": [
"Hao Yu",
"Aoran Gan",
"Kai Zhang",
"Shiwei Tong",
"Qi Liu",
"Zhaofeng Liu"
],
"year": 2025,
"date": "2025-01-01",
"type": "conference-paper",
"doi": "10.1007/978-981-96-1024-2_8",
"doiUrl": "https://doi.org/10.1007/978-981-96-1024-2_8",
"citedBy": 375,
"venue": "Communications in computer and information science",
"isOpenAccess": true,
"pdfUrl": "https://arxiv.org/pdf/2405.07437",
"landingUrl": "https://doi.org/10.1007/978-981-96-1024-2_8",
"abstract": null,
"openalexId": "https://openalex.org/W4406771321",
"source": "openalex",
"query": "retrieval augmented generation",
"scrapedAt": "2026-11-12T22:57:24Z"
},
{
"title": "Benchmarking Retrieval-Augmented Generation for Medicine",
"authors": [
"Guangzhi Xiong",
"Qiao Jin",
"Zhiyong Lu",
"Aidong Zhang"
],
"year": 2024,
"date": "2024-01-01",
"type": "conference-paper",
"doi": "10.18653/v1/2024.findings-acl.372",
"doiUrl": "https://doi.org/10.18653/v1/2024.findings-acl.372",
"citedBy": 539,
"venue": "Findings of the Association for Computational Linguistics: ACL (Findings of ACL)",
"isOpenAccess": true,
"pdfUrl": "https://aclanthology.org/2024.findings-acl.372.pdf",
"landingUrl": "https://doi.org/10.18653/v1/2024.findings-acl.372",
"abstract": "While large language models (LLMs) have achieved state-of-the-art performance on a wide range of medical question answering (QA) tasks, they still face challenges with hallucinatio",
"openalexId": "https://openalex.org/W4402670290",
"source": "openalex",
"query": "retrieval augmented generation",
"scrapedAt": "2026-11-12T22:57:24Z"
}
]
}
curl --location --request GET 'https://zylalabs.com/api/14867/scientific+papers+api/38700/scientific+papers+api?queries=retrieval augmented generation&openAccessOnly=false&sort=relevance&source=auto&maxItems=5' --header 'Authorization: Bearer YOUR_API_KEY'
साइन अप करने के बाद, प्रत्येक डेवलपर को एक पर्सनल API एक्सेस की असाइन की जाती है, जो अक्षरों और अंकों का एक यूनिक संयोजन होता है, जिसका उपयोग हमारे API एंडपॉइंट तक पहुंचने के लिए किया जाता है। प्रमाणीकरण के लिए वैज्ञानिक पत्र API के साथ बस अपने बेयरर टोकन को Authorization हेडर में शामिल करें।
| हेडर | विवरण |
|---|---|
Authorization
|
आवश्यक
होना चाहिए Bearer access_key. जब आप सब्सक्राइब हों तो ऊपर "Your API Access Key" देखें।
|
कोई लंबी अवधि की प्रतिबद्धता नहीं। कभी भी अपग्रेड, डाउनग्रेड या कैंसल करें।
कीवर्ड, लेखक या DOI द्वारा लाखों शोध पत्रों की खोज करें। जब एक मौजूद हो तो शीर्षक, लेखक, वर्ष, स्थान, सारांश, उद्धरण संख्या, DOI और एक ओपन-एक्सेस PDF लिंक वापस करता है, जैसे कि साफ JSON में
कीवर्ड, लेखक या DOI द्वारा लाखों शोध पत्रों की खोज करें शीर्षक, लेखकों, वर्ष, स्थान, सारांश, उद्धरण की संख्या, DOI और एक मुक्त-प्रवेश PDF लिंक जब वह मौजूद हो, के रूप में साफ JSON लौटाता है
प्रत्येक अनुरोध पर 100 तक के परिणाम अधिकतमItems पैरामीटर के साथ सेट करें
प्रत्येक अनुरोध सार्वजनिक स्रोत से लाइव डेटा इकट्ठा करता है समान अनुरोधों को 6 घंटे तक cache से सेवा दी जा सकती है
सूची पैरामीटर कई मानों को स्वीकार करते हैं जो अल्पविराम द्वारा अलग किए गए हैं उदाहरण के लिए लंदन पेरिस