RAG / 检索增强生成

Comparing the Performance of LLMs in RAG-based Question-Answering: A Case Study in Computer Science Literature

Ranul Dayarathne, Uvini Ranaweera, Upeksha Ganegoda

机构 * University of Moratuwa（穆塔瓦大学） 纠错

专题命中 RAG评测 ：RAG(title,abstract)；retrieval augmented generation(abstract)；分类 cs.CL、cs.AI

Comments 18 pages, 4 figures, 5 tables, presented at the 5th International Conference on Artificial Intelligence in Education Technology

Journal ref Lecture Notes on Data Engineering and Communications Technologies, vol. 228, Springer, 2025, pp. 387--403

Beyond Synthetic Benchmarks: Evaluating LLM Performance on Real-World Class-Level Code Generation

Musfiqur Rahman, SayedHassan Khatoonabadi, Emad Shihab

机构 * Concordia University（康科德大学） 纠错

专题命中 RAG评测 ：retrieval-augmented generation(abstract)；分类 cs.AI

Comments Pre-print submitted for reviwer to TOSEM