Enhancing Retrieval-Augmented Generation: A Study of Best Practices (2025.coling-main)
Copied to clipboard
| 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. |
| Approach: | They propose to integrate query expansion, various novel retrieval strategies, and a Contrastive In-Context Learning RAG to improve response quality. |
| Outcome: | The proposed RAGs incorporate query expansion, various novel retrieval strategies, and a novel Contrastive In-Context Learning RAG. |
Similar Papers
Searching for Best Practices in Retrieval-Augmented Generation (2024.emnlp-main)
Copied to clipboard
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
| Challenge: | Retrieval-augmented generation (RAG) techniques have proven to be effective in integrating up-to-date information, mitigating hallucinations, and enhancing response quality, especially in specialized domains. |
| Approach: | They propose several strategies for deploying RAG that balance performance and efficiency. |
| Outcome: | The proposed approaches can significantly enhance question-answering capabilities and accelerate the generation of multimodal content using a “retrieval as generation” strategy. |
On the Influence of Context Size and Model Choice in Retrieval-Augmented Generation Systems (2025.findings-naacl)
Copied to clipboard
| Challenge: | Retrieval-augmented generation (RAG) is an approach to augment large language models (LLMs) despite their impressive performance, LLMs can generate plausible sounding but factually incorrect responses (hallucinations) |
| Approach: | They propose to use BM25 and semantic search as retrievers to augment large language models by reducing their reliance on static knowledge and improving answer factuality. |
| Outcome: | The proposed approach improves QA performance on a biomedical task with up to 15 snippets but stagnates or declines beyond that. |
Retrieval-augmented Generation across Heterogeneous Knowledge (2022.naacl-srw)
Copied to clipboard
| Challenge: | Existing methods for retrieving knowledge from a single source homogeneous corpus have been gaining increasing attention in the field of natural language processing (NLP) however, they still suffer from the following drawbacks: (i) They are usually trained offline, making the model agnostic to the latest information, e.g., asking a chat-bot about COVID-19. |
| Approach: | They propose to use a single-source homogeneous corpus to generate retrieval-augmented generation models that can learn from the pre-training corpus. |
| Outcome: | The proposed methods have been applied to various knowledge-intensive NLP tasks, but most of the work has focused on retrieving unstructured text documents from Wikipedia. |
A Survey of RAG-Reasoning Systems in Large Language Models (2025.findings-emnlp)
Copied to clipboard
Yangning Li, Weizhi Zhang, Yuyao Yang, Wei-Chieh Huang, Yaozu Wu, Junyu Luo, Yuanchen Bei, Henry Peng Zou, Xiao Luo, Yusheng Zhao, Chunkit Chan, Yankai Chen, Zhongfen Deng, Yinghui Li, Hai-Tao Zheng, Dongyuan Li, Renhe Jiang, Ming Zhang, Yangqiu Song, Philip S. Yu
| Challenge: | a survey of RAG-based reasoning-based approaches shows that it is not effective for multi-step inferences. |
| Approach: | They map how advanced reasoning optimizes each stage of RAG . they show how retrieved knowledge supply missing premises and expand context for complex inference . |
| Outcome: | The proposed frameworks achieve state-of-the-art across knowledge-intensive benchmarks. |
RAG+: Enhancing Retrieval-Augmented Generation with Application-Aware Reasoning (2025.emnlp-main)
Copied to clipboard
Yu Wang, Shiwan Zhao, Zhihu Wang, Ming Fan, Xicheng Zhang, Yubo Zhang, Zhengfan Wang, Heyuan Huang, Ting Liu
| Challenge: | Existing RAG paradigms often overlook the cognitive step of applying knowledge, leaving a gap between retrieved facts and task-specific reasoning. |
| Approach: | They introduce a module extension that integrates application-aware reasoning into the RAG pipeline. |
| Outcome: | Experiments show that RAG+ outperforms standard RAG variants and achieves gains of 3–5% in complex scenarios. |
Know Your RAG: Dataset Taxonomy and Generation Strategies for Evaluating RAG Systems (2025.coling-industry)
Copied to clipboard
Rafael Teixeira de Lima, Shubham Gupta, Cesar Berrospi Ramis, Lokesh Mishra, Michele Dolfi, Peter Staar, Panagiotis Vagenas
| Challenge: | Retrieval Augmented Generation (RAG) systems are widespread in the industry. |
| Approach: | They propose to use Q&A datasets to assess retrieval performance and label-targeted data generation to refine RAG datasets. |
| Outcome: | The proposed system can generate Q&A datasets with fine-tuned small LLMs. |
Is Agentic RAG worth it? An experimental comparison of RAG approaches (2026.acl-industry)
Copied to clipboard
| 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. |
| Approach: | They propose to use a "Enhanced" RAG to address weaknesses in the workflow . they propose to orchestrate the entire process, deciding which actions to perform, when to perform them, and whether to iterate . |
| Outcome: | The proposed models address shortcomings in the RAG workflow, and provide practical insights into the trade-offs between them. |
Towards Knowledge Checking in Retrieval-augmented Generation: A Representation Perspective (2025.naacl-long)
Copied to clipboard
Shenglai Zeng, Jiankun Zhang, Bingheng Li, Yuping Lin, Tianqi Zheng, Dante Everaert, Hanqing Lu, Hui Liu, Hui Liu, Yue Xing, Monica Xiao Cheng, Jiliang Tang
| Challenge: | Existing studies have shown that LLMs struggle to identify the boundaries of their own knowledge and tend to prioritize external information over internal knowledge learned during pre-training. |
| Approach: | They conduct a comprehensive analysis of LLM representation behaviors and demonstrate the significance of using representations in knowledge checking. |
| Outcome: | The proposed classifiers improve performance even when dealing with noisy knowledge databases. |
HASH-RAG: Bridging Deep Hashing with Retriever for Efficient, Fine Retrieval and Augmented Generation (2025.findings-acl)
Copied to clipboard
| Challenge: | Experimental evaluations on NQ, TriviaQA, and HotpotQA datasets demonstrate that our approach achieves a 90% reduction in retrieval time compared to conventional methods while maintaining considerate recall performance. |
| Approach: | They propose a framework that integrates deep hashing techniques with systematic optimizations to address these limitations. |
| Outcome: | The proposed framework outperforms retrieval/non-retrieval baselines by 1.4-4.3% in EM scores on NQ, TriviaQA, and HotpotQA datasets. |
How Retrieved Context Shapes Internal Representations in RAG (2026.findings-acl)
Copied to clipboard
| Challenge: | Retrieval-augmented generation (RAG) is a widely adopted approach for enhancing large language models with external knowledge. |
| Approach: | They analyze how different types of retrieved documents affect the hidden states of large language models and how these internal representation shifts relate to downstream generation behavior. |
| Outcome: | The results show that context relevancy and layer-wise processing influence internal representations, providing explanations of LLMs’ output behaviors and insights for RAG system design. |