Enhancing Reliability in Community Question Answering with an Expert-Oriented RAG System (2026.findings-eacl)
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| Challenge: | Existing LLMs require substantial computational resources and are prone to generating hallucinated or unreliable content. |
| Approach: | They propose an expert-oriented Retrieval-Augmented Generation framework which leverages user modeling to identify archived questions with answers that fully or partially address the user’s new query. |
| Outcome: | The proposed framework synthesizes expert-written answers from similar questions to generate unified answers. |
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| Challenge: | Retrieval-augmented generation (RAG) is effective for question answering tasks . multi-hop questions, such as "Which company among NVIDIA, Apple, and Google made the biggest profit in 2023?" challenge RAG because relevant facts are often distributed across multiple documents . |
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| Challenge: | Existing methods to integrate Large Language Models with external knowledge suffer from limited reasoning capabilities, especially when using open-source LLMs. |
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Is Agentic RAG worth it? An experimental comparison of RAG approaches (2026.acl-industry)
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| Challenge: | Retrieval-Augmented Generation (RAG) systems have several limitations, including noisy or suboptimal retrieval, misuse of retrieval for out-of-scope queries, weak query–document matching, and variability or cost associated with the generator. |
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A Survey of RAG-Reasoning Systems in Large Language Models (2025.findings-emnlp)
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Know Your RAG: Dataset Taxonomy and Generation Strategies for Evaluating RAG Systems (2025.coling-industry)
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MES-RAG: Bringing Multi-modal, Entity-Storage, and Secure Enhancements to RAG (2025.findings-naacl)
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Enhancing Retrieval-Augmented Generation: A Study of Best Practices (2025.coling-main)
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| Challenge: | Retrieval-augmented generation systems have shown remarkable advancements by integrating retrieval mechanisms into language models, enhancing their ability to produce more accurate and contextually relevant responses. |
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RAG in the Wild: On the (In)effectiveness of LLMs with Mixture-of-Knowledge Retrieval Augmentation (2026.findings-acl)
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| Challenge: | Retrieval-augmented generation (RAG) enhances large language models by integrating external knowledge retrieved at inference time. |
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Searching for Best Practices in Retrieval-Augmented Generation (2024.emnlp-main)
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Xiaohua Wang, Zhenghua Wang, Xuan Gao, Feiran Zhang, Yixin Wu, Zhibo Xu, Tianyuan Shi, Zhengyuan Wang, Shizheng Li, Qi Qian, Ruicheng Yin, Changze Lv, Xiaoqing Zheng, Xuanjing Huang
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RAGLAB: A Modular and Research-Oriented Unified Framework for Retrieval-Augmented Generation (2024.emnlp-demo)
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Xuanwang Zhang, Yun-Ze Song, Yidong Wang, Shuyun Tang, Xinfeng Li, Zhengran Zeng, Zhen Wu, Wei Ye, Wenyuan Xu, Yue Zhang, Xinyu Dai, Shikun Zhang, Qingsong Wen
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