Papers with RAG pipeline
Question Decomposition for Retrieval-Augmented Generation (2025.acl-srw)
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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 . |
| Approach: | They propose a pipeline that incorporates question decomposition to ground large language models in verifiable external sources. |
| Outcome: | The proposed approach improves retrieval and answer accuracy over standard RAG . multi-hop questions often require multiple documents to support the model . |
SuperRAG: Beyond RAG with Layout-Aware Graph Modeling (2025.naacl-industry)
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| Challenge: | Existing methods that only deal with flat text chunks use a graph structure to handle complex questions. |
| Approach: | They propose layout-aware graph modeling for multimodal RAG using document layout parsing to take into account relationship of multimodalities. |
| Outcome: | The proposed method can handle complex questions that require information from multimodalities. |
OmniEval: An Omnidirectional and Automatic RAG Evaluation Benchmark in Financial Domain (2025.emnlp-main)
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| Challenge: | a new benchmark for RAG is developed for the financial domain . omnidirectional and automatic benchmarks are difficult to build in vertical domains . |
| Approach: | They propose an omnidirectional and automatic RAG benchmark for the financial domain . they categorize RAG scenarios by task classes and 16 financial topics . |
| Outcome: | The proposed benchmark achieves an 87.47% acceptance ratio in human evaluations of generated instances. |
LumberChunker: Long-Form Narrative Document Segmentation (2024.findings-emnlp)
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| Challenge: | Modern NLP tasks rely on dense retrieval methods to access up-to-date and relevant contextual information. |
| Approach: | They propose a method that leverages an LLM to dynamically segment documents by iterating on a set of sequential passages to identify the point where the content begins to shift. |
| Outcome: | The proposed method outperforms the most competitive baseline by 7.37% in retrieval performance and integrates into a RAG pipeline. |
Fine-grained Knowledge Enhancement for Retrieval-Augmented Generation (2025.findings-acl)
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| Challenge: | Existing studies rely on semantic similarity to retrieve knowledge but ignore fine-grained information within documents. |
| Approach: | They propose a fine-grained knowledge enhancement method to fill knowledge gaps with retrieved external information by a Chain-of-Thought prompting procedure and a decoding enhancement strategy to constrain the document-based decoding process. |
| Outcome: | The proposed method can be applied in a plug-and-play manner to enhance its performance with no additional modules or training process. |
Does RAG Introduce Unfairness in LLMs? Evaluating Fairness in Retrieval-Augmented Generation Systems (2025.coling-main)
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| Challenge: | Retrieval-Augmented Generation (RAG) models address fairness concerns with respect to sensitive attributes such as gender, geographic location, and other demographic factors. |
| Approach: | They propose a framework to evaluate fairness in RAG using scenario-based questions and analyzing disparities across demographic attributes. |
| Outcome: | The proposed framework analyzes disparities across demographic attributes and identifies fairness issues in retrieval and generation stages. |
DAVIS: Planning Agent with Knowledge Graph-Powered Inner Monologue (2025.findings-emnlp)
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| Challenge: | Existing approaches to designing a generalist scientific agent fail to address multifaceted requirements of scientific tasks. |
| Approach: | DAVIS is a generalist scientific agent capable of performing tasks in laboratory settings to assist researchers. |
| Outcome: | DAVIS performs better on ScienceWorld benchmarks compared to previous approaches on 8 out of 9 elementary science subjects. |
AutoGraph-R1: End-to-End Reinforcement Learning for Knowledge Graph Construction (2026.acl-long)
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Hong Ting Tsang, Jiaxin Bai, Haoyu Huang, Qiao Xiao, Tianshi Zheng, Baixuan Xu, Shujie Liu, Yangqiu Song
| Challenge: | Currently, knowledge graphs are decoupled from their downstream application, resulting in suboptimal graph structures. |
| Approach: | They propose a framework to directly optimize KG construction for task performance using Reinforcement Learning (RL). |
| Outcome: | The proposed framework improves performance across multiple QA benchmarks and consistently achieves significant performance gains over task-agnostic baseline graphs. |
A Comparison of Independent and Joint Fine-tuning Strategies for Retrieval-Augmented Generation (2025.findings-emnlp)
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| Challenge: | Multiple fine-tuning strategies exist with different costs and benefits for RAG pipelines. |
| Approach: | They evaluate several RAG fine-tuning strategies with different costs and benefits . embedding and generator models can be fine- tuned to increase performance . |
| Outcome: | The proposed techniques improve quality metrics, but have different computational costs. |
DeKeyNLU: Enhancing Natural Language to SQL Generation through Task Decomposition and Keyword Extraction (2025.findings-emnlp)
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Jian Chen, Zhenyan Chen, Xuming Hu, Peilin Zhou, Yining Hua, Han Fang, Cissy Hing Yee Choy, Xinmei Ke, Jingfeng Luo, Zixuan Yuan
| Challenge: | NL2SQL provides a model-centric paradigm that simplifies database access for non-technical users . challenges such as inaccurate task decomposition and keyword extraction remain major bottlenecks . |
| Approach: | They propose a RAG-based NL2SQL pipeline that employs three modules for query understanding, entity retrieval, and generation to improve SQL generation accuracy. |
| Outcome: | The proposed pipeline improves the accuracy of query generation on BIRD and Spider datasets. |
HiChunk: Evaluating and Enhancing Retrieval Augmented Generation with Hierarchical Chunking (2026.acl-long)
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| Challenge: | Existing evaluation benchmarks for document chunking are inadequate due to evidence sparsity . evaluators are unable to evaluate different chunking methods due to the evidence sparing . |
| Approach: | They propose a QA benchmark for document chunking and a hierarchical document structuring framework for it. |
| Outcome: | The proposed framework improves document chunking quality within reasonable time consumption. |
LLM-Generated Text May Harm Your Retrieval! A Robust Detection Strategy for Retrieval-Augmented Generation (2026.acl-long)
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| Challenge: | Retrieval-augmented generation (RAG) improves accuracy and timeliness of large language models, but external corpora may become contaminated with LLM-generated texts. |
| Approach: | They propose a method that integrates external knowledge retrieved from external sources into RAG to filter out LLM-generated texts from retrieved results. |
| Outcome: | The proposed method mitigates performance degradation and improves stability of RAG systems. |
RAG+: Enhancing Retrieval-Augmented Generation with Application-Aware Reasoning (2025.emnlp-main)
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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. |