RETRIEVAL-AUGMENTED GENERATION

Unsupervised Information Refinement Training of Large Language Models for Retrieval-Augmented Generation (2024.acl-long)

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.

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Shicheng Xu, Liang Pang, Mo Yu, Fandong Meng, Huawei Shen, Xueqi Cheng, Jie Zhou

R³A: Reinforced Reasoning for Relevance Assessment for RAG in User-Generated Content Platforms (2026.acl-industry)

Challenge:	Existing studies show that LLMs face challenges in effectively using retrieved information . authors propose a method that considers LLM as "Information Refiner"
Approach:	They propose a method that considers LLMs as "Information Refiners" they propose INFO-RAG, which is low-cost and general across various tasks .
Outcome:	The proposed method improves performance of LLaMA2 by 9.39% relative points . it is low-cost and general across various tasks, and is robust and in-context learning is possible .

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Xiaowei Yuan, Lei Jin, Haoxin Zhang, Ziyang Huang, Yan Gao, null Yiwu, Yao Hu, Jun Zhao, Kang Liu

Thesis Proposal: On the Granularity-Robustness Trade-off in Text-Derived Knowledge Graphs (2026.acl-srw)

Challenge:	Existing approaches to query–document relevance assessment are limited . ambiguous user intent and asymmetric relevance are challenges for RAG platforms .
Approach:	They propose a decomposed reasoning model for relevance assessment that decomposes query intent into intent inference and evidence grounding.
Outcome:	The proposed model outperforms strong baselines on offline benchmarks and achieves significant gains in large-scale online A/B testing.

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Surawat Pralomram

Question Decomposition for Retrieval-Augmented Generation (2025.acl-srw)

Challenge:	Retrieval-augmented generation (RAG) based on dense embeddings is a dominant paradigm for text retrieval, but many real-world applications require attribute-specific querying.
Approach:	They propose a query-driven framework for constructing and retrieving knowledge graphs from text using dense embeddings.
Outcome:	The proposed framework combines the robustness of dense retrieval with the explicit queryability of symbolic representations.

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Paul J. L. Ammann, Jonas Golde, Alan Akbik

RAGViz: Diagnose and Visualize Retrieval-Augmented Generation (2024.emnlp-demo)

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 .

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Tevin Wang, Jingyuan He, Chenyan Xiong

Multilingual Retrieval-Augmented Generation for Knowledge-Intensive Question Answering Task (2026.findings-eacl)

Challenge:	Large language models (LLMs) lack domain-specific knowledge and can cause hallucinations.
Approach:	They propose a RAG diagnosis tool that visualizes the attentiveness of the generated tokens in retrieved documents.
Outcome:	RAGViz provides token and document-level attention visualization and generation comparison upon context document addition and removal.

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Leonardo Ranaldi, Barry Haddow, Alexandra Birch

Multilinguality

Challenge:	Existing studies focus on English as the data language for RAG, resulting in limited coverage of multilingual RAG.
Approach:	They propose a method that translates retrieved documents into a common language before generating the response.
Outcome:	The proposed approach improves efficiency on knowledge-intensive tasks but introduces inconsistencies due to cross-lingual variations in the retrieved content.

Ai2 Scholar QA: Organized Literature Synthesis with Attribution (2025.acl-demo)

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Amanpreet Singh, Joseph Chee Chang, Dany Haddad, Aakanksha Naik, Jena D. Hwang, Rodney Kinney, Daniel S Weld, Doug Downey, Sergey Feldman

PrismRAG: Boosting RAG Factuality with Distractor Resilience and Strategized Reasoning (2025.emnlp-industry)

Challenge:	Ai2 Scholar QA is a free online scientific question answering application . it uses retrieval-augmented generation to answer complex scientific questions . many of these systems are expensive to use and closed-source .
Approach:	They propose a retrieval-augmented generation-based scientific question answering application . it uses a Python package and an interactive web app to make the entire pipeline public . they compare it with other similar question-answering applications .
Outcome:	The proposed system outperforms other systems on a recent scientific QA benchmark.

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Mohammad Kachuee, Teja Gollapudi, Minseok Kim, Yin Huang, Kai Sun, Xiao Yang, Jiaqi Wang, Nirav Shah, Yue Liu, Aaron Colak, Anuj Kumar, Wen-tau Yih, Xin Luna Dong

Benchmarking LLM Faithfulness in RAG with Evolving Leaderboards (2025.emnlp-industry)

Challenge:	Existing methods to improve factuality of large language models (LLMs) rely on human-engineered instructions.
Approach:	They propose a retrieval-augmented generation framework that trains the model with distractor-aware QA pairs mixing gold evidence with subtle distractor passages and instills reasoning-centric habits that make the LLM plan, rationalize, and synthesize without extensive human engineered instructions.
Outcome:	The proposed framework outperforms state-of-the-art solutions across 12 open-book RAG QA benchmarks and is being deployed in production.

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Manveer Singh Tamber, Forrest Sheng Bao, Chenyu Xu, Ge Luo, Suleman Kazi, Minseok Bae, Miaoran Li, Ofer Mendelevitch, Renyi Qu, Jimmy Lin

Evaluating Cost-Efficiency of LLMs in a RAG Setup on Polish Wikipedia: Quality vs. Energy Consumption (2026.eacl-srw)

Challenge:	Large language models (LLMs) excel in various tasks, but often produce hallucinations . retrieved contexts, misrepresent information, or generate outright contradictions .
Approach:	They propose a framework that measures hallucination faithfulness of large language models . they introduce a leaderboard that leverages diverse human-annotated hallucinian examples .
Outcome:	The proposed framework improves hallucination evaluations by leveraging human-annotated examples.

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Patrycja Smits, Tomasz Walkowiak

DRAGON: Domain-specific Robust Automatic Data Generation for RAG Optimization (2026.findings-eacl)

Challenge:	Retrieval-augmented generation systems are a dominant paradigm for knowledge-intensive applications.
Approach:	They evaluate language models from 4B to 70B parameters within a Polish Wikipedia-based RAG pipeline.
Outcome:	The proposed model selection process reduces energy consumption by 83% and improves quality.

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Haiyang Shen, Hang Yan, Zhongshi Xing, Mugeng Liu, Yue Li, Zhiyang Chen, Yuxiang Wang, Jiuzheng Wang, Yun Ma

An Information Bottleneck Perspective for Effective Noise Filtering on Retrieval-Augmented Generation (2024.acl-long)

Challenge:	Existing retrieval-augmented generation paradigms rely heavily on public knowledge . Existing RAGs reliant on public information and often falter when faced with domain-specific queries.
Approach:	They propose a framework that combines a data-construction modeling approach with a scalable synthetic data-generation pipeline to optimize domain-specific retrieval performance.
Outcome:	The proposed framework optimizes domain-specific retrieval performance and bolsters retriever robustness.

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Kun Zhu, Xiaocheng Feng, Xiyuan Du, Yuxuan Gu, Weijiang Yu, Haotian Wang, Qianglong Chen, Zheng Chu, Jingchang Chen, Bing Qin

Respecting Temporal-Causal Consistency: Entity-Event Knowledge Graph for Retrieval-Augmented Generation (2026.eacl-long)

Challenge:	Experimental results show that retrieval-augmented generation improves accuracy and relevance of large language models.
Approach:	They propose to introduce the information bottleneck theory into retrieval-augmented generation by maximizing mutual information between compression and ground output while minimizing mutual information .
Outcome:	The proposed approach improves accuracy and correctness of answer generation and conciseness with 2.5% compression rate.

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Ze Yu Zhang, Zitao Li, Yaliang Li, Bolin Ding, Bryan Kian Hsiang Low

When Facts Change: Temporal Knowledge Conflict Resolution in LLMs (2026.findings-acl)

Challenge:	Standard unstructured RAG methods rely on embedding-similarity matching and lack any general mechanism to encode or exploit chronological information.
Approach:	They propose a retrieval-augmented generation framework that integrates a document retrieval generator with an exter-nal document retriever to enhance the model's accuracy.
Outcome:	The proposed framework outperforms state-of-the-art unstructured and KG-based RAG frameworks on causal and character consistency queries.

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Jonas Wallat, Wolfgang Nejdl, Sandipan Sikdar

Fanar-Sadiq: A Multi-Agent Architecture for Grounded Islamic QA (2026.acl-industry)

Challenge:	Large language models are increasingly used in retrieval-augmented generation systems to reconcile knowledge conflicts between parametric memory and contextual inputs.
Approach:	They propose to use mutability to resolve temporal misalignment in large language models to compare stable and recently updated facts from Wikidata to determine if mutable models can serve as a mediating signal in this process.
Outcome:	The proposed model can produce reasoning for facts that actually changed but rarely for stable ones, whereas smaller models rarely detect conflict, while larger models detect it but fail to act on mutability judgments.

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Ummar Abbas, Mourad Ouzzani, Mohamed Y. Eltabakh, Omar Sinan, Gagan Bhatia, Hamdy Mubarak, Majd Hawasly, Mohammed Qusay Hashim, Kareem Mohamed Darwish, Firoj Alam

RAG-HAT: A Hallucination-Aware Tuning Pipeline for LLM in Retrieval-Augmented Generation (2024.emnlp-industry)

Challenge:	Large language models (LLMs) can answer religious knowledge queries fluently, but they often hallucinate and misattribute sources.
Approach:	They propose a bilingual Arabic-English Islamic QA system that uses a multi-agent, tool-augmented architecture to route Islamic queries to specialized modules.
Outcome:	The proposed system is based on a multi-agent, tool-augmented architecture and has received over 1.9M accesses in less than a year.

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Juntong Song, Xingguang Wang, Juno Zhu, Yuanhao Wu, Xuxin Cheng, Randy Zhong, Cheng Niu

Controlling Risk of Retrieval-augmented Generation: A Counterfactual Prompting Framework (2024.findings-emnlp)

Challenge:	Retrieval-augmented generation (RAG) has emerged as a significant advancement in the field of large language models (LLMs).
Approach:	They propose a method that uses hallucination detection labels to correct hallucines by integrating up-to-date information into their initial training.
Outcome:	The proposed method is based on the Retrieval Augmented Generation (RAG) method, which has shown to be effective in mitigating hallucinations and improving answer quality.

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Lu Chen, Ruqing Zhang, Jiafeng Guo, Yixing Fan, Xueqi Cheng

Low-Resource NLP

Challenge:	Existing studies on retrieval-augmented generation (RAG) rarely address the issue of predictive uncertainty, i.e., how likely it is that a RAG model’s prediction is incorrect.
Approach:	They propose a framework that induces RAG models to alter latent factors and analyzes the effect on their answers.
Outcome:	The proposed framework identifies two critical factors affecting RAG models' confidence in their answers and analyzes the effect on their answers.

ColMate: Contrastive Late Interaction and Masked Text for Multimodal Document Retrieval (2025.emnlp-industry)

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Ahmed Masry, Megh Thakkar, Patrice Bechard, Sathwik Tejaswi Madhusudhan, Rabiul Awal, Shambhavi Mishra, Akshay Kalkunte Suresh, Srivatsava Daruru, Enamul Hoque, Spandana Gella, Torsten Scholak, Sai Rajeswar

Classifying and Addressing the Diversity of Errors in Retrieval-Augmented Generation Systems (2026.eacl-long)

Challenge:	Existing methods for multimodal document retrieval often replicate techniques developed for text-only retrieval.
Approach:	They propose a document retrieval model that bridges the gap between multimodal representation learning and document retrievals by providing external knowledge as context.
Outcome:	The proposed model achieves 3.61% improvement over existing retrieval models on the ViDoRe V2 benchmark, showing stronger generalization to out-of-domain benchmarks.

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Kin Kwan Leung, Mouloud Belbahri, Yi Sui, Alex Labach, Xueying Zhang, Stephen Anthony Rose, Jesse C. Cresswell

DF-RAG: Query-Aware Diversity for Retrieval-Augmented Generation (2026.findings-eacl)

Challenge:	Existing work on RAG errors has not accounted for the complexity of real-world RAG systems and their failure modes.
Approach:	They propose a taxonomy of error types that can occur in realistic RAG systems and an auto-evaluation method that can be used to track errors during development.
Outcome:	The proposed method can be used in practice to track and address errors during development.

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Saadat Hasan Khan, Spencer Hong, Jingyu Wu, Kevin Lybarger, Youbing Yin, Erin Babinsky, Daben Liu

RAG-Critic: Leveraging Automated Critic-Guided Agentic Workflow for Retrieval Augmented Generation (2025.acl-long)

Challenge:	Retrieval-augmented generation (RAG) is a common technique for grounding language models in domain-specific information.
Approach:	They propose a new retrieval technique that incorporates diversity into the retrieval step to improve performance on reasoning-intensive QA benchmarks.
Outcome:	The proposed method outperforms baselines on reasoning-intensive QA benchmarks by 4–10%.

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Guanting Dong, Jiajie Jin, Xiaoxi Li, Yutao Zhu, Zhicheng Dou, Ji-Rong Wen

FinMRAGBench: A Realistic and Complex Benchmark for Multi-Modal RAG in Financial Document Analysis (2026.findings-acl)

Challenge:	Recent advances in large language models (LLMs) have demonstrated remarkable performance across a wide range of downstream tasks.
Approach:	They propose a framework that leverages a critic-guided agentic workflow to improve RAG capabilities autonomously.
Outcome:	The proposed framework improves RAG capabilities autonomously by leveraging a critic-guided agentic workflow.

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Shouqing Yang, Qi Zhang, Yuhang Yang, Ruikang Xu, Yuwei Hou, Zhulin Jia, Lirong Gao, Haobo Wang, Jinglei Chen, Jiexiang Wang, Sheng Guo, Bo Zheng, Gang Chen

Eval-RAR: Evaluation-Driven Retrieval-Augmented Reasoning via Reinforcement Learning (2026.findings-acl)

Challenge:	Existing benchmarks for realistic financial analysis fail to capture realistic financial situations involving cross-document retrieval, multi-page evidence integration, and diverse analytical tasks.
Approach:	They propose a multi-modal financial RAG benchmark that evaluates large language models in realistic financial analysis settings.
Outcome:	The proposed framework achieves the strongest overall performance across all models.

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Heng Yu, Rui Li, Qi Liu, Wenjun Feng, Junfeng Kang, Yi Zhan

RemoteRAG: A Privacy-Preserving LLM Cloud RAG Service (2025.findings-acl)

Challenge:	Existing methods for retrieval-augmented generation fail to provide explicit supervision for internal reasoning process.
Approach:	They propose an Evaluation-driven Retrieval-Augmented Reasoning framework that uses reinforcement learning and a fine-grained evaluation reward to optimize the process.
Outcome:	Eval-RAR outperforms existing methods on QA benchmarks on seven single-hop and multi-hop tasks.

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Yihang Cheng, Lan Zhang, Junyang Wang, Mu Yuan, Yunhao Yao

EfficientRAG: Efficient Retriever for Multi-Hop Question Answering (2024.emnlp-main)

Challenge:	Large language models (LLMs) have a tendency to generate factually incorrect or purely fictional responses, a phenomenon known as hallucination.
Approach:	They propose to use remote RAG to protect user query from privacy leakage . they introduce (n,)-DistanceDP to characterize privacy leakages of user query .
Outcome:	The proposed solution can resist embedding inversion attacks while achieving no loss in retrieval under various settings.

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Ziyuan Zhuang, Zhiyang Zhang, Sitao Cheng, Fangkai Yang, Jia Liu, Shujian Huang, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang, Qi Zhang

Tricking Retrievers with Influential Tokens: An Efficient Black-Box Corpus Poisoning Attack (2025.naacl-long)

Challenge:	Existing retrieval-augmented generation methods rely on multiple calls of large language models (LLMs) Large-language models lack knowledge underrepresented in training data and still face hallucinations.
Approach:	They propose an efficient retriever for multi-hop question answering that generates new queries iteratively without the need for LLM calls.
Outcome:	The proposed method surpasses existing methods on three open-domain multi-hop question-answering datasets.

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Cheng Wang, Yiwei Wang, Yujun Cai, Bryan Hooi

How Does Knowledge Selection Help Retrieval Augmented Generation? (2025.findings-emnlp)

Challenge:	Existing methods for crafting adversarial passages are slow and computationally expensive, requiring either access to retriever’s gradients or large computational resources.
Approach:	They propose a method that leverages two key characteristics of retrievers: insensitivity to token order and bias towards influential tokens to generate effective adversarial passages.
Outcome:	The proposed method achieves superior efficiency and scalability compared to existing methods while maintaining comparable or better attack success rates across multiple datasets.

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Xiangci Li, Jessica Ouyang

Investigating Context Faithfulness in Large Language Models: The Roles of Memory Strength and Evidence Style (2025.findings-acl)

Challenge:	Retrieval-augmented generation (RAG) is a powerful method for enhancing natural language generation by integrating external knowledge into a model’s output.
Approach:	They empirically analyze how knowledge selection influences downstream generation performance in RAG systems by simulating different retrieval and selection conditions through a controlled mixture of gold and distractor knowledge.
Outcome:	The proposed model is based on a controlled mixture of gold and distractor knowledge and simulated with a gold and distractors.

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Yuepei Li, Kang Zhou, Qiao Qiao, Bach Nguyen, Qing Wang, Qi Li

Compete to Complete: Co-opetition Adversarial Learning for Retrieval-Augmented Generation (2026.acl-long)

Challenge:	Retrieval-augmented generation improves Large Language Models (LLMs) by integrating external information into the response generation process.
Approach:	They investigate the impact of memory strength and evidence presentation on LLMs’ receptiveness to external evidence by measuring the divergence in LLM responses to different paraphrases of the same question.
Outcome:	The proposed method improves Large Language Models (LLMs) by integrating external information into the response generation process.

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Xin Liu, Yu-An Liu, Ruqing Zhang, Yixing Fan, Lixin Su, Jiafeng Guo, Xueqi Cheng

DVD: Dynamic Contrastive Decoding for Knowledge Amplification in Multi-Document Question Answering (2024.emnlp-main)

Challenge:	Existing approaches to reduce hallucination in large language models lack a robust mechanism for generating a generative model.
Approach:	They propose a framework that formulates retriever–generator training in RAG as a minimax game.
Outcome:	The proposed framework improves retrieval-augmented generation performance on seven benchmark datasets.

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Jing Jin, Houfeng Wang, Hao Zhang, Xiaoguang Li, Zhijiang Guo

The Good and The Bad: Exploring Privacy Issues in Retrieval-Augmented Generation (RAG) (2024.findings-acl)

Challenge:	Large language models (LLMs) generate information with hallucinations due to uneven retrieval quality and irrelevant contents.
Approach:	They propose a decoding strategy which dynamically amplifies knowledge from selected documents during the generation phase.
Outcome:	The proposed method outperforms other decoding strategies on ALCE-ASQA, NQ, TQA and PopQA benchmarks.

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Shenglai Zeng, Jiankun Zhang, Pengfei He, Yiding Liu, Yue Xing, Han Xu, Jie Ren, Yi Chang, Shuaiqiang Wang, Dawei Yin, Jiliang Tang

HIRAG: Hierarchical-Thought Instruction-Tuning Retrieval-Augmented Generation (2025.findings-emnlp)

Challenge:	Retrieval-augmented generation (RAG) is a powerful technique to facilitate language model generation with proprietary and private data, where data privacy is . a privacy issue that is currently under-explored, is posed by RAG.
Approach:	They propose to use retrieval-augmented generation (RAG) to facilitate language model generation with proprietary and private data where data privacy is a pivotal concern.
Outcome:	The proposed attack methods demonstrate that RAG can mitigate the old risks, i.e., leakage of the LLMs’ training data.

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Yihan Jiao, Zhehao Tan, Dan Yang, Duolin Sun, Jie Feng, Yue Shen, Jian Wang, Peng Wei

LONG2RAG: Evaluating Long-Context & Long-Form Retrieval-Augmented Generation with Key Point Recall (2024.findings-emnlp)

Challenge:	In-depth research on the specific capabilities needed by the RAG generation model is lacking, leading to inconsistent document quality and retrieval system imperfections.
Approach:	They propose that RAG models should possess three progressively hierarchical abilities: (1) Filtering: the ability to select relevant information; (2) Combination: the capability to combine semantic information across paragraphs; (3) RAG-specific reasoning: the capacity to further process external knowledge using internal knowledge.
Outcome:	Experiments show that the proposed method significantly improves the model’s open-book examination capability on datasets such as RGB, PopQA, MuSiQue, HotpotQA, and PubmedQA.

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Zehan Qi, Rongwu Xu, Zhijiang Guo, Cunxiang Wang, Hao Zhang, Wei Xu

RA-ISF: Learning to Answer and Understand from Retrieval Augmentation via Iterative Self-Feedback (2024.findings-acl)

Challenge:	Retrieval-augmented generation (RAG) is a promising approach to address limitations of fixed knowledge in large language models.
Approach:	They propose a benchmark and a metric to assess LLMs' ability to generate long-form responses that exploit retrieved information.
Outcome:	The proposed benchmarks lack a comprehensive evaluation method to assess LLMs' ability to generate long-form responses that effectively exploits retrieved information.

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Yanming Liu, Xinyue Peng, Xuhong Zhang, Weihao Liu, Jianwei Yin, Jiannan Cao, Tianyu Du

OmniEval: An Omnidirectional and Automatic RAG Evaluation Benchmark in Financial Domain (2025.emnlp-main)

Challenge:	Large language models (LLMs) have demonstrated excellent performance in numerous tasks but the parameterized knowledge stored within LLMs may be incomplete and hard to incorporate up-to-date knowledge.
Approach:	They propose a framework that iteratively decomposes tasks and processes them in three submodules to enhance the model’s problem-solving capabilities.
Outcome:	The proposed method outperforms existing benchmarks on GPT3.5, Llama2 and other large language models significantly enhancing factual reasoning capabilities and reducing hallucinations.

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Shuting Wang, Jiejun Tan, Zhicheng Dou, Ji-Rong Wen

Tackling Distractor Documents in Multi-Hop QA with Reinforcement and Curriculum Learning (2026.findings-eacl)

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.

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Jerry Huang, Siddarth Madala, Risham Sidhu, Cheng Niu, Hao Peng, Julia Hockenmaier, Tong Zhang

BRIEF: Bridging Retrieval and Inference for Multi-hop Reasoning via Compression (2025.findings-naacl)

Challenge:	Existing work on retrieval-augmented generation systems has shown that retrievers exhibit imperfect recall and precision, limiting downstream performance.
Approach:	They propose a retrieval-augmented generation model that generates answers from larger sets of retrieved contexts.
Outcome:	The proposed model generates answers and cites relevant information from larger sets of retrieved contexts.

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Yuankai Li, Jia-Chen Gu, Di Wu, Kai-Wei Chang, Nanyun Peng

Chain-of-Rank: Enhancing Large Language Models for Domain-Specific RAG in Edge Device (2025.findings-naacl)

Challenge:	Existing approaches to augment language models with external knowledge but they are limited by static nature of pre-training data.
Approach:	They propose a lightweight approach that compresses retrieved documents into highly dense textual summaries to integrate into in-context RAG.
Outcome:	The proposed approach reduces latency and costs while achieving high performance in open-domain questions.

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Juntae Lee, Jihwan Bang, Kyuhong Shim, Seunghan Yang, Simyung Chang

Retrieval-Augmented Machine Translation with Unstructured Knowledge (2025.findings-emnlp)

Challenge:	Retrieval-augmented generation (RAG) is valuable in specialized domains where precision is critical.
Approach:	They propose a chain-of-rank algorithm which allows LLMs to access a target domain early via finetuning.
Outcome:	The proposed method achieves state-of-the-art in benchmarks and analyzes its efficacy.

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Jiaan Wang, Fandong Meng, Yingxue Zhang, Jie Zhou

Text Generation

Challenge:	Retrieval-augmented generation (RAG) is a new approach to enhance large language models (LLMs).
Approach:	They propose a multi-task training method to teach LLMs how to use information from multilingual documents during their translation.
Outcome:	The proposed method improves LLMs by 1.6-3.1 BLEU and 1.0-2.0 COMET scores in En-Zh, and 1.7-2.9 BLUE and 2.1-2.7 COMET score in En de.

Query Decomposition for RAG: Balancing Exploration-Exploitation (2026.eacl-long)

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Roxana Petcu, Kenton Murray, Daniel Khashabi, Evangelos Kanoulas, Maarten de Rijke, Dawn Lawrie, Kevin Duh

On the Influence of Context Size and Model Choice in Retrieval-Augmented Generation Systems (2025.findings-naacl)

Challenge:	Complex user queries often involve the exclusion of information, negation, or missing entities.
Approach:	They propose to decompose user requests into subqueries, retrieve potentially relevant documents for each and then aggregate them to generate an answer.
Outcome:	The proposed method achieves 35% gain in document-level precision and 15% increase in -nDCG . it also improves the downstream task of long-form generation.

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Juraj Vladika, Florian Matthes

AutoEvolve: Automatically Evolving Queries for Applicable and Scalable Retrieval-Augmented Generation Benchmarking (2025.findings-emnlp)

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.

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Ding-Chu Zhang, Xiaowen Zhang, Yue Fei, Renjun Hu, Xiao-Wen Yang, Zhi Zhou, Baixuan Li, Yu-Feng Li, Xing Shi, Wei Lin

Inference Scaling for Bridging Retrieval and Augmented Generation (2025.findings-naacl)

Challenge:	Existing automated generation methods exhibit Weak Applicability and Weak Scalability . existing methods are limited by their reliance on metadata from specific corpora .
Approach:	They propose an approach to generate scalable RAG benchmarks using corpus-agnostic methods . they propose a difficulty-guided metric that directs query evolution process .
Outcome:	The proposed approach evolves queries significantly more challenging than existing methods . it is able to dynamically increase difficulty, limiting scalability of the query .

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Youngwon Lee, Seung-won Hwang, Daniel F Campos, Filip Graliński, Zhewei Yao, Yuxiong He

Document Segmentation Matters for Retrieval-Augmented Generation (2025.findings-acl)

Challenge:	Existing work observed the generator bias, such that improving the retrieval results may negatively affect the outcome.
Approach:	They propose to use inference scaling to aggregate inference calls from the permuted order of retrieved contexts to create a new ranking.
Outcome:	The proposed approach improves ROUGE-L on MS MARCO and EM on HotpotQA benchmarks by 7 points.

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Zhitong Wang, Cheng Gao, Chaojun Xiao, Yufei Huang, Shuzheng Si, Kangyang Luo, Yuzhuo Bai, Wenhao Li, Tangjian Duan, Chuancheng Lv, Guoshan Lu, Gang Chen, Fanchao Qi, Maosong Sun

Syntactic Text Processing

Challenge:	Existing rule-based chunking methods lead to suboptimal splits, where overly large chunks introduce irrelevant information and small chunks lack semantic coherence.
Approach:	They propose a method that leverages document summaries as pseudo-instructions to guide chunking by computing semantic similarity between sentences and the summary.
Outcome:	Experiments on multiple open-domain question-answering benchmarks show that PIC significantly improves retrieval accuracy (Hits@k) and end-to-end QA performance (Exact Match) without any additional training.

Word2Passage: Word-level Importance Re-weighting for Query Expansion (2025.findings-acl)

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Jeonghwan Choi, Minjeong Ban, Minseok Kim, Hwanjun Song

Safeguarding Privacy of Retrieval Data against Membership Inference Attacks: Is This Query Too Close to Home? (2025.findings-emnlp)

Challenge:	Retrieval-augmented generation (RAG) enhances the quality of LLM generation by providing relevant chunks, but retrieving accurately from external knowledge remains challenging due to missing contextually important words in query expansion.
Approach:	They propose a method that generates word, sentence, and passage references for query expansion and assigns distinct importance scores to words based on their origin and characteristics.
Outcome:	The proposed method outperforms existing methods across datasets and LLM configurations, effectively enhancing retrieval accuracy and generation quality.

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Yujin Choi, Youngjoo Park, Junyoung Byun, Jaewook Lee, Jinseong Park

AttnComp: Attention-Guided Adaptive Context Compression for Retrieval-Augmented Generation (2025.findings-emnlp)

Challenge:	delivering private retrieved documents directly to LLMs introduces vulnerability to membership inference attacks .
Approach:	They propose a similarity-based membership inference attack detection framework for RAG . they propose obfuscate attackers, maintain data utility, and remain system-agnostic .
Outcome:	The proposed framework can detect and hide membership inference attacks, while remaining system-agnostic against them.

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Lvzhou Luo, Yixuan Cao, Ping Luo

Knowledge Graph-Guided Retrieval Augmented Generation (2025.naacl-long)

Challenge:	Existing methods for augmented large language models suffer from irrelevant retrieved content . existing methods struggle to adapt compression rates for different context, maintain low latency .
Approach:	We propose an adaptive, efficient and context-aware compression framework to reduce retrieved content . AttnComp uses a top-p compression algorithm to retain the minimal set of documents whose attention weights exceed a threshold.
Outcome:	Experiments show that AttnComp outperforms existing compression methods and uncompressed baselines in achieving higher accuracy with substantial compression rates and lower latency.

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Xiangrong Zhu, Yuexiang Xie, Yi Liu, Yaliang Li, Wei Hu

Structured Data in NLP

Challenge:	Existing studies on RAG focus on semantic retrieval of isolated relevant chunks, which ignore their intrinsic relationships.
Approach:	They propose a framework that utilizes knowledge graphs to provide fact-level relationships between chunks, improving the diversity and coherence of the retrieved results.
Outcome:	Extensive experiments on the HotpotQA dataset and its variants demonstrate the advantages of KG2RAG compared to existing RAG-based approaches in terms of response quality and retrieval quality.

PA-RAG: RAG Alignment via Multi-Perspective Preference Optimization (2025.naacl-long)

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Jiayi Wu, Hengyi Cai, Lingyong Yan, Hao Sun, Xiang Li, Shuaiqiang Wang, Dawei Yin, Ming Gao

Improving Multilingual Retrieval-Augmented Language Models through Dialectic Reasoning Argumentations (2025.emnlp-main)

Challenge:	Existing approaches to optimize RAG generators fail to align with RAG requirements thoroughly.
Approach:	They propose a method for optimizing the RAG generator from multiple preference perspectives to align with RAG requirements comprehensively.
Outcome:	The proposed method improves the performance of RAG generators by incorporating retrieved documents into the prompt.

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Leonardo Ranaldi, Federico Ranaldi, Fabio Massimo Zanzotto, Barry Haddow, Alexandra Birch

TRACE the Evidence: Constructing Knowledge-Grounded Reasoning Chains for Retrieval-Augmented Generation (2024.findings-emnlp)

Challenge:	Existing approaches to ground large language models (LLMs) with RAGs are limited by the heterogeneity of knowledge retrieved.
Approach:	They propose a modular approach guided by Argumentative Explanations that evaluates retrieved information by comparing, contrasting and resolving conflicting perspectives.
Outcome:	The proposed framework significantly improves RAG approaches, requiring low-impact computational effort and providing robustness to knowledge perturbations.

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Jinyuan Fang, Zaiqiao Meng, Craig MacDonald

Assessing “Implicit” Retrieval Robustness of Large Language Models (2024.emnlp-main)

Challenge:	Existing retrievers are not perfect and often include irrelevant documents in the retrieved set.
Approach:	They propose to construct knowledge-grounded reasoning chains from retrieved documents to integrate supporting evidence into RAG models.
Outcome:	The proposed model achieves an average performance improvement of 14.03% on three multi-hop QA datasets.

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Xiaoyu Shen, Rexhina Blloshmi, Dawei Zhu, Jiahuan Pei, Wei Zhang

WebWalker: Benchmarking LLMs in Web Traversal (2025.acl-long)

Challenge:	Retrieval-augmented generation (RAG) is a framework to enhance large language models with external knowledge, but its effectiveness is constrained by the retrieval robustness of the model.
Approach:	They propose to use gold and distracting context to fine-tune models to handle relevant or irrelevant retrieved context in an end-to-end manner.
Outcome:	The proposed model performs better when gold and distracting context are used, while still extracting correct answers when retrieval is accurate.

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Jialong Wu, Wenbiao Yin, Yong Jiang, Zhenglin Wang, Zekun Xi, Runnan Fang, Linhai Zhang, Yulan He, Deyu Zhou, Pengjun Xie, Fei Huang

Uplift-RAG: Uplift-Driven Knowledge Preference Alignment for Retrieval-Augmented Generation (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) have demonstrated impressive capabilities across a wide range of natural language processing tasks.
Approach:	They propose a benchmark to assess the ability of LLMs to perform web traversal by using an explore-critic paradigm.
Outcome:	The proposed framework mimics human-like web navigation through an explore-critic paradigm and demonstrates the effectiveness of RAG combined with WebWalker in real-world scenarios.

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Changle Qu, Sunhao Dai, Hengyi Cai, Yiyang Cheng, Jun Xu, Shuaiqiang Wang, Dawei Yin

Fine-grained Knowledge Enhancement for Retrieval-Augmented Generation (2025.findings-acl)

Challenge:	Existing efforts to estimate document utility rely on downstream generation performance, which conflates the influence of external documents with the intrinsic knowledge of the LLM.
Approach:	They propose an uplift-based definition of document utility that quantifies each document’s marginal benefit over the LLM’s internal knowledge.
Outcome:	The proposed framework improves the performance of the LLM by incorporating external retrieved documents into the model.

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Jingxuan Han, Zhendong Mao, Yi Liu, Yexuan Che, Zheren Fu, Quan Wang

QPaug: Question and Passage Augmentation for Open-Domain Question Answering of LLMs (2024.findings-emnlp)

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.

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Minsang Kim, Cheoneum Park, Seung Baek

Enhancing Noise Robustness of Retrieval-Augmented Language Models with Adaptive Adversarial Training (2024.acl-long)

Challenge:	Existing approaches to augmented generation of retrieved passages rely on the quality of a question's retrieved information.
Approach:	They propose a simple yet efficient method called question and passage augmentation via LLMs for open-domain QA.
Outcome:	The proposed method outperforms the state-of-the-art and achieves significant performance gain over existing methods.

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Feiteng Fang, Yuelin Bai, Shiwen Ni, Min Yang, Xiaojun Chen, Ruifeng Xu

Bridging External and Parametric Knowledge: Mitigating Hallucination of LLMs with Shared-Private Semantic Synergy in Dual-Stream Knowledge (2025.emnlp-main)

Challenge:	Large language models (LLMs) exhibit substantial capabilities yet face challenges such as hallucination, outdated knowledge, and untraceable reasoning processes.
Approach:	They propose a retrieval-augmented generation approach that leverages adaptive adversarial training to dynamically adjust the model’s training process in response to retrieval noises.
Outcome:	The proposed approach improves the performance of the LLaMA-2 7B model under diverse noise conditions.

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Yi Sui, Chaozhuo Li, Chen Zhang, Dawei Song, Qiuchi Li

Eliciting Critical Reasoning in Retrieval-Augmented Generation via Contrastive Explanations (2025.naacl-long)

Challenge:	Retrieval-augmented generation (RAG) aims to mitigate the hallucination of Large Language Models (LLMs) however, external knowledge may contain noise and conflict with parametric knowledge of LLMs, leading to degraded performance.
Approach:	They propose a Dual-Stream Knowledge-Augmented Framework for Shared-Private Semantic Synergy that refines the traditional self-attention into a mixed-attention that distinguishes shared and private semantics for a controlled knowledge integration.
Outcome:	Extensive experiments show that the proposed framework achieves a superior performance over baselines.

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Leonardo Ranaldi, Marco Valentino, Andre Freitas

SimRAG: Self-Improving Retrieval-Augmented Generation for Adapting Large Language Models to Specialized Domains (2025.naacl-long)

Challenge:	Recent studies show that LLMs struggle to critically analyse RAG-based in-context information.
Approach:	They propose a framework that elicits critical arguments in RAG via contrastive explanations . they propose CRAG to retrieve relevant documents given a query and generate explanations that explicitly contrast relevance of passages to support the final answer.
Outcome:	The proposed framework improves state-of-the-art RAG models while requiring significantly fewer prompts and demonstrations and robust to perturbations in the retrieved documents.

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Ran Xu, Hui Liu, Sreyashi Nag, Zhenwei Dai, Yaochen Xie, Xianfeng Tang, Chen Luo, Yang Li, Joyce C. Ho, Carl Yang, Qi He

CoDA: Restoring Contextual Dominance via Copy-Encouraged Attention Intervention for Mitigating RAG Hallucinations (2026.findings-acl)

Challenge:	Retrieval-augmented generation (RAG) enhances the question answering abilities of large language models (LLMs) however, adapting general-purpose RAG systems to specialized fields poses unique challenges due to distribution shifts and limited access to domain-specific data.
Approach:	They propose a method that equips large language models with joint capabilities of question answering and question generation for domain adaptation.
Outcome:	Experiments on 11 datasets across three different domains verify the efficacy of SimRAG over baselines by 1.2%–8.6%.

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JinWei Shi, Qizhuo Xie, Qianzi Hou, Zhipeng Wang, Wanting Su, Jianhua Zhao, Tao Zheng, Tieke He

RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models (2024.acl-long)

Challenge:	Retrieval-augmented generation reduces hallucination by grounding outputs in external evidence.
Approach:	They propose a lightweight inference-time attention intervention that amplifies evidence-aligned value states to enhance contextual faithfulness and reduce hallucination.
Outcome:	The proposed model reduces hallucination by grounding model outputs in external evidence.

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Cheng Niu, Yuanhao Wu, Juno Zhu, Siliang Xu, KaShun Shum, Randy Zhong, Juntong Song, Tong Zhang

Separate the Wheat from the Chaff: Winnowing Down Divergent Views in Retrieval Augmented Generation (2025.emnlp-main)

Challenge:	Retrieval-augmented generation (RAG) is a main technique for alleviating hallucinations in large language models.
Approach:	They propose to integrate RAG into large language models to analyze word-level hallucinations using a corpus of 18,000 naturally generated responses from diverse LLMs.
Outcome:	The proposed model can fine tune a relatively small LLM and achieve a competitive hallucination detection performance when compared to the existing prompt-based approaches.

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Song Wang, Zihan Chen, Peng Wang, Zhepei Wei, Zhen Tan, Yu Meng, Cong Shen, Jundong Li

Text Clustering

Challenge:	Large language models (LLMs) lack robustness in knowledge-intensive tasks due to noisy or irrelevant retrieved data.
Approach:	They propose a multi-agent debate-based RAG framework that integrates external knowledge sources into large language models to improve their accuracy.
Outcome:	The proposed framework is unsupervised and leverages pretrained LLMs without fine-tuning, making it easily adaptable to various tasks.

Who’s Who: Large Language Models Meet Knowledge Conflicts in Practice (2024.findings-emnlp)

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Quang Pham, Hoang Ngo, Anh Tuan Luu, Dat Quoc Nguyen

Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy (2023.findings-emnlp)

Challenge:	Recent large-scale pretrained language models excel in tasks requiring natural language understanding, but they often "hallucinate" plausible but incorrect content due to outdated or incorrect pretraining information.
Approach:	They propose a public benchmark dataset to examine model’s behavior in knowledge conflict situations.
Outcome:	The proposed model induces conflicts by asking about a common property among entities having the same name, resulting in questions with up to 8 distinctive answers.

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Zhihong Shao, Yeyun Gong, Yelong Shen, Minlie Huang, Nan Duan, Weizhu Chen

CtrlA: Adaptive Retrieval-Augmented Generation via Inherent Control (2025.findings-acl)

Challenge:	Recent work has proposed to improve relevance modeling by having large language models actively involved in retrieval, i.e., to guide retrieval with generation.
Approach:	They propose to have large language models actively involved in retrieval to guide retrieval with generation.
Outcome:	The proposed method synergizes retrieval and generation in an iterative manner, and can generate better results in subsequent iterations.

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Liu Huanshuo, Hao Zhang, Zhijiang Guo, Jing Wang, Kuicai Dong, Xiangyang Li, Yi Quan Lee, Cong Zhang, Yong Liu

SARA: Selective and Adaptive Retrieval-augmented Generation with Context Compression (2026.acl-long)

Challenge:	Existing methods focus on detecting LLM’s confidence via statistical uncertainty.
Approach:	They propose to use a representation perspective to solve adaptive RAG by enabling dynamic retrieval during generation and enabling retrieval only when the query exceeds LLM's internal knowledge.
Outcome:	The proposed framework is superior to existing adaptive RAG methods on a diverse set of tasks.

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Yiqiao Jin, Kartik Sharma, Vineeth Rakesh, Yingtong Dou, Menghai Pan, Mahashweta Das, Srijan Kumar

MoLoRAG: Bootstrapping Document Understanding via Multi-modal Logic-aware Retrieval (2025.emnlp-main)

Challenge:	Retrieval-augmented generation (RAG) extends large language models with external knowledge, but it must balance limited effective context, redundant retrieved evidence, and the loss of fine-grained facts.
Approach:	They propose a hybrid RAG framework that uses natural-language snippets and semantic compression vectors to preserve passages in text form and compress remaining evidence into interpretable vectors for iterative evidence reranking.
Outcome:	The proposed framework improves answer relevance, answer correctness and semantic similarity across 9 datasets and 5 open-source LLMs.

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Xixi Wu, Yanchao Tan, Nan Hou, Ruiyang Zhang, Hong Cheng

HydraRAG: Structured Cross-Source Enhanced Large Language Model Reasoning (2025.emnlp-main)

Challenge:	Document Understanding is a foundational AI capability with broad applications . Large Vision-Language Models (LLMs) can't handle multi-page document comprehension . a logic-aware retrieval framework for multi-modal, multi- page document understanding is proposed .
Approach:	They propose a logic-aware retrieval framework for multi-modal, multi-page document understanding . MoLoRAG uses semantic and logical relevance to deliver more accurate retrieval .
Outcome:	The proposed framework improves on four DocQA datasets and demonstrates 9.68% accuracy improvement over existing methods.

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Xingyu Tan, Xiaoyang Wang, Qing Liu, Xiwei Xu, Xin Yuan, Liming Zhu, Wenjie Zhang

SimpleDeepSearcher: Deep Information Seeking via Web-Powered Reasoning Trajectory Synthesis (2025.findings-emnlp)

Challenge:	Current RAG system retrieves evidence from knowledge graphs and text documents but has limitations in multi-hop reasoning, multi-entity questions, and source verification.
Approach:	They propose a training-free framework that unifies graph topology, document semantics, and source reliability to support deep, faithful reasoning in large language models.
Outcome:	The proposed framework outperforms the current hybrid model-based model-driven system by 20.3% and 30.1% on seven benchmark datasets.

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Shuang Sun, Huatong Song, Yuhao Wang, Ruiyang Ren, Jinhao Jiang, Junjie Zhang, Fei Bai, Jia Deng, Xin Zhao, Zheng Liu, Lei Fang, Zhongyuan Wang, Ji-Rong Wen

RichRAG: Crafting Rich Responses for Multi-faceted Queries in Retrieval-Augmented Generation (2025.coling-main)

Challenge:	Existing approaches to deep search training lack high-quality training trajectories, prohibitive computational costs and lack of high-fidelity training data.
Approach:	They propose a framework that synthesizes high-quality training data by simulating real user interactions in live web search environments.
Outcome:	The proposed framework synthesizes high-quality training data by simulating user interactions in live web search environments.

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Shuting Wang, Xin Yu, Mang Wang, Weipeng Chen, Yutao Zhu, Zhicheng Dou

How Retrieved Context Shapes Internal Representations in RAG (2026.findings-acl)

Challenge:	Existing studies focus on question scenarios with clear user intents and concise answers, but it is prevalent that users issue broad, open-ended queries with diverse sub-intents.
Approach:	They propose a framework that includes a sub-aspect explorer and a multi-faceted retriever to build a candidate pool of diverse external documents related to these sub-intents.
Outcome:	The proposed framework provides comprehensive and satisfying responses to users on two publicly available datasets.

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Samuel Yeh, Sharon Li

Representation Learning

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.

Your RAG is Unfair: Exposing Fairness Vulnerabilities in Retrieval-Augmented Generation via Backdoor Attacks (2025.emnlp-main)

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Gaurav Bagwe, Saket Sanjeev Chaturvedi, Xiaolong Ma, Xiaoyong Yuan, Kuang-Ching Wang, Lan Emily Zhang

Optimizing Multi-Hop Document Retrieval Through Intermediate Representations (2025.findings-acl)

Challenge:	Retrieval-augmented generation (RAG) enhances factual grounding but introduces new attack surfaces, particularly through backdoor attacks.
Approach:	They propose a framework that exposes fairness vulnerabilities in RAG through a two-phase backdoor attack.
Outcome:	Empirical results show that BiasRAG achieves high attack success rates while remaining undetectable under standard fairness evaluations.

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Linjiaen Linjiaen, Jingyu Liu, Yingbo Liu

HiKEY: Hierarchical Multimodal Retrieval for Open-Domain Document Question Answering (2026.acl-long)

Challenge:	Existing approaches to addressing multi-hop queries are computationally expensive . despite their success, large language models often generate factually incorrect answers .
Approach:	They propose a layer-by-layer reasoning approach that leverages intermediate representations from the middle layers to retrieve external knowledge.
Outcome:	The proposed method outperforms existing RAG methods on open-domain multi-hop question-answering datasets while maintaining inference overhead similar to that of standard RAG.

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Joongmin Shin, Gyuho Shim, Jeongbae Park, Jaehyung Seo, Heuiseok Lim

RetroLLM: Empowering Large Language Models to Retrieve Fine-grained Evidence within Generation (2025.acl-long)

Challenge:	Existing approaches to document-based Opendomain Question Answering (ODQA) use flat text chunks or page-level images to locate the correct document.
Approach:	They propose a hierarchical tree-based multimodal retrieval framework that elevates document hierarchy to a first-class retrieval signal.
Outcome:	The proposed framework outperforms page- and chunk-based baselines on ODQA benchmarks and improves retrieval recall by 12.9% and end-to-end QA performance by 6.8%.

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Xiaoxi Li, Jiajie Jin, Yujia Zhou, Yongkang Wu, Zhonghua Li, Ye Qi, Zhicheng Dou

EventRAG: Enhancing LLM Generation with Event Knowledge Graphs (2025.acl-long)

Challenge:	Existing methods rely on separate retrievers to fetch top-k text chunks for generating evidence, and they lack joint optimization.
Approach:	They propose a framework that integrates retrieval and generation into a single, auto-regressive process, enabling LLMs to directly generate fine-grained evidence from the corpus with constrained decoding.
Outcome:	Extensive experiments on five open-domain QA datasets demonstrate the proposed framework’s superior performance across both in-domain and out-of-domain tasks.

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Zairun Yang, Yilin Wang, Zhengyan Shi, Yuan Yao, Lei Liang, Keyan Ding, Emine Yilmaz, Huajun Chen, Qiang Zhang

RAG in the Wild: On the (In)effectiveness of LLMs with Mixture-of-Knowledge Retrieval Augmentation (2026.findings-acl)

Challenge:	Existing approaches to text generation often neglect event structures that shape real-world narratives.
Approach:	They propose a framework that integrates structured event semantics with iterative retrieval and inference to enhance text generation.
Outcome:	Experiments on UltraDomain and MultiHopRAG show that the proposed framework outperforms baseline RAG systems in generation effectiveness, logical consistency, and multi-hop reasoning accuracy.

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Ran Xu, Yuchen Zhuang, Yue Yu, Haoyu Wang, Wenqi Shi, Carl Yang

Hierarchical Retrieval with Evidence Curation for Open-Domain Financial Question Answering on Standardized Documents (2025.findings-acl)

Challenge:	Retrieval-augmented generation (RAG) enhances large language models by integrating external knowledge retrieved at inference time.
Approach:	They evaluate RAG systems using MassiveDS, a large-scale datastore with mixture of knowledge.
Outcome:	The proposed approach improves performance on knowledge-intensive NLP tasks.

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Jaeyoung Choe, Jihoon Kim, Woohwan Jung

GNN-RAG: Graph Neural Retrieval for Efficient Large Language Model Reasoning on Knowledge Graphs (2025.findings-acl)

Challenge:	standardized documents share similar formats and table structures . this similarity forces traditional RAG methods to misidentify near-duplicate text .
Approach:	They propose a hierarchical retrieval framework that performs hierarchically to reduce confusion among similar texts.
Outcome:	The proposed framework reduces confusion among similar documents by removing irrelevant passages . it generates complementary queries to collect missing information .

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Costas Mavromatis, George Karypis

Optimizing Question Semantic Space for Dynamic Retrieval-Augmented Multi-hop Question Answering (2025.acl-long)

Challenge:	Existing approaches to retrieval-augmented generation (RAG) rely on costly LLM calls to generate relation paths or traverse the KG.
Approach:	They propose a framework that uses lightweight Graph Neural Networks to enhance retrieval.
Outcome:	The proposed framework outperforms existing methods on multi-hop and multi-entity questions.

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Linhao Ye, Lang Yu, Zhikai Lei, Qin Chen, Jie Zhou, Liang He

Familiarity-Aware Evidence Compression for Retrieval-Augmented Generation (2025.findings-emnlp)

Challenge:	Existing retrieval-augmented generation methods are insufficient for multi-hop question answering . however, they tend to generate hallucinations due to semantic mismatching .
Approach:	They propose to optimize question semantic space for dynamic retrieval-augmented multi-hop question answering by optimizing the semantic embeddings.
Outcome:	The proposed method outperforms existing RAG methods in both in- and out-of-domain settings.

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Dongwon Jung, Qin Liu, Tenghao Huang, Ben Zhou, Muhao Chen

Knowledge Poisoning Attacks on Medical Multi-Modal Retrieval-Augmented Generation (2026.acl-long)

Challenge:	Retrieval-augmented generation (RAG) improves large language models by incorporating non-parametric knowledge through evidence retrieved from external sources.
Approach:	They propose a training-free evidence compression technique that makes retrieved evidence more familiar to the target model while seamlessly integrating parametric knowledge from the model.
Outcome:	The proposed technique outperforms the most recent evidence compression baselines across open-domain QA datasets while achieving high compression rates.

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Peiru Yang, Haoran Zheng, Tong Ju, Shiting Wang, Wanchun Ni, Jiajun Liu, Shangguang Wang, Yongfeng Huang, Tao Qi

R^3AG: Retriever Routing for Retrieval-Augmented Generation (2026.acl-long)

Challenge:	Existing studies have investigated knowledge poisoning attacks in medical RAG systems . knowledge poison attacks can disrupt model outputs and undermine system reliability .
Approach:	They propose a knowledge poisoning framework that injects misinformation into textual data . they propose to use paired visual data as a query-agnostic trigger to promote retrieval .
Outcome:	The proposed framework produces clinically plausible but incorrect generations on five LLMs and datasets.

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Tong Zhao, Yutao Zhu, Yucheng Tian, Zhicheng Dou

A Reality Check on Context Utilisation for Retrieval-Augmented Generation (2025.acl-long)

Challenge:	Retrieval-augmented generation (RAG) is often bottlenecked by the “one-size-fits-all” retrieval paradigm, as different queries exhibit distinct preferences for different retrievers.
Approach:	They propose a novel routing framework that explicitly models the dynamic alignment between queries and retriever capabilities and decomposes retriever capability into two learnable dimensions: retrieval quality and generation utility.
Outcome:	Experiments on knowledge-intensive tasks show that R3AG outperforms both the best individual retrievers and state-of-the-art static routing methods.

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Lovisa Hagström, Sara Vera Marjanovic, Haeun Yu, Arnav Arora, Christina Lioma, Maria Maistro, Pepa Atanasova, Isabelle Augenstein

Searching for Best Practices in Retrieval-Augmented Generation (2024.emnlp-main)

Challenge:	Existing studies on LM context utilisation of retrieved information have focused on synthetic text.
Approach:	They propose a dataset of unreliable, insufficient and difficult-to-understand contexts with real-world queries and contexts manually annotated for stance to compare them to synthetic datasets.
Outcome:	The proposed model outperforms synthetic datasets and exaggerates rare context characteristics, leading to inflated context utilisation results.

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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

Efficient Context Selection for Long-Context QA: No Tuning, No Iteration, Just Adaptive‐k (2025.emnlp-main)

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.

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Chihiro Taguchi, Seiji Maekawa, Nikita Bhutani

Self-Correcting RAG: Enhancing Faithfulness via MMKP Context Selection and NLI-Guided MCTS (2026.findings-acl)

Challenge:	Existing adaptive methods struggle with aggregation QA where optimal external context is unknown and variable.
Approach:	They propose a single-pass method that selects a query-specific number of passages . Adaptivek retrieval matches or outperforms fixedk baselines while using 10x fewer tokens compared to full-context input .
Outcome:	Adaptivek retrieval matches or outperforms fixedk baselines on factoid and aggregation QA benchmarks . it uses 10x fewer tokens than full-context input and still retrieves 70% of relevant passages compared to previous methods .

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Shijia Xu, Zhou Wu, Xiaolong Jia, Yu Wang, Kai Liu, April Xiaowen Dong

s3: You Don’t Need That Much Data to Train a Search Agent via RL (2025.emnlp-main)

Challenge:	Existing approaches to retrieval-augmented generation still face problems with low context utilization and frequent hallucinations.
Approach:	They propose a framework that reformulates retrieval and generation as constrained optimization and path planning.
Outcome:	The proposed framework significantly improves reasoning accuracy on complex queries while reducing hallucinations.

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Pengcheng Jiang, Xueqiang Xu, Jiacheng Lin, Jinfeng Xiao, Zifeng Wang, Jimeng Sun, Jiawei Han

Controlled Retrieval-augmented Context Evaluation for Long-form RAG (2025.findings-emnlp)

Challenge:	Existing approaches to optimize retrieval using search-only metrics ignore downstream utility and fine-tune entire LLM to jointly reason and retrieve limit retrieval utility and compatibility with frozen or proprietary models.
Approach:	They propose a lightweight, model-agnostic framework that decouples the searcher from the generator and trains the search user using a Gain Beyond RAG reward.
Outcome:	The proposed framework outperforms baselines trained on over 70 more data with 2.4k training samples.

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Jia-Huei Ju, Suzan Verberne, Maarten de Rijke, Andrew Yates

Graph of Records: Boosting Retrieval Augmented Generation for Long-context Summarization with Graphs (2025.acl-long)

Challenge:	Retrieval-augmented generation (RAG) enhances large language models by incorporating context retrieved from external knowledge sources.
Approach:	They propose a Controlled Retrieval-aUgmented conteXt evaluation framework to directly assess retrieval-augmented contexts.
Outcome:	The proposed framework uses human-written summaries to control the information scope of knowledge.

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Haozhen Zhang, Tao Feng, Jiaxuan You

Retrieval-Augmented Language Models are Mimetic Theorem Provers (2025.findings-emnlp)

Challenge:	Existing methods for retrieving historical LLM responses are lacking in long-context summarization tasks.
Approach:	They propose a graph of records which leverages historical LLM responses to enhance RAG for long-context global summarization.
Outcome:	The proposed method improves on four long-context summarization datasets.

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Wenjie Yang, Ruiyuan Huang, Jiaxing Guo, Zicheng Lyu, Tongshan Xu, Shengzhong Zhang, Lun Du, Da Zheng, Zengfeng Huang

CompAct: Compressing Retrieved Documents Actively for Question Answering (2024.emnlp-main)

Challenge:	Large language models often fail to provide rigorous proof-based reasoning for research-level mathematics.
Approach:	They propose a simple yet effective RAG framework that augments retrieved proofs with queries and document contexts to improve retrieval performance.
Outcome:	The proposed framework improves retrieval performance by 34.19% . dual RAG can be used to prove research-level theorems in theoretical machine learning .

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Chanwoong Yoon, Taewhoo Lee, Hyeon Hwang, Minbyul Jeong, Jaewoo Kang

Question Answering

Challenge:	Existing methods to condense extensive documents with no loss of information are difficult to implement in real-world scenarios.
Approach:	They propose a framework that employs an active strategy to condense extensive documents without losing key information.
Outcome:	The proposed framework improves performance and compression rate on multi-hop question-answering benchmarks.

Benchmarking Foundation Models with Retrieval-Augmented Generation in Olympic-Level Physics Problem Solving (2025.findings-emnlp)

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Shunfeng Zheng, Yudi Zhang, Meng Fang, Zihan Zhang, Zhitan Wu, Mykola Pechenizkiy, Ling Chen

Mitigating the Privacy Issues in Retrieval-Augmented Generation (RAG) via Pure Synthetic Data (2025.emnlp-main)

Challenge:	a new study examines the potential of retrieval-augmented generation (RAG) with foundation models to enhance expert-level reasoning.
Approach:	They introduce PhoPile, a high-quality multimodal dataset specifically designed for Olympiad-level physics.
Outcome:	The proposed model can be used to solve Olympiad-level physics problems.

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Shenglai Zeng, Jiankun Zhang, Pengfei He, Jie Ren, Tianqi Zheng, Hanqing Lu, Han Xu, Hui Liu, Yue Xing, Jiliang Tang

Enhancing RAG Efficiency with Adaptive Context Compression (2025.findings-emnlp)

Challenge:	Existing literature suggests that RAG systems may face privacy issues when the retrieval process involves private data.
Approach:	They propose a two-stage synthetic data generation paradigm that uses attributes to preserve contextual information from the original data.
Outcome:	The proposed approach preserves key contextual information from the original data while reducing privacy risks.

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Shuyu Guo, Shuo Zhang, Zhaochun Ren

Green & Sustainable NLP

Challenge:	Existing methods apply fixed compression rates, over-compressing simple queries or under-compressed complex ones.
Approach:	a new framework uses a hierarchical compressor and a context selector to optimize inference efficiency . a framework that dynamically adjusts compression rates based on input complexity optimizes inference without loss of accuracy.
Outcome:	Adaptive Context Compression for RAG outperforms fixed-rate methods on Wikipedia and five QA datasets .

Beyond Chunks and Graphs: Retrieval-Augmented Generation through Triplet-Driven Thinking (2026.findings-acl)

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Shengbo Gong, Xianfeng Tang, Qi He, Carl Yang, Wei Jin

GRAT: Guiding Retrieval-Augmented Reasoning through Process Rewards Tree Search (2025.acl-long)

Challenge:	Retrieval-augmented generation (RAG) is critical for reducing hallucinations and incorporating external knowledge into Large Language Models (LLMs).
Approach:	They propose a framework that leverages an LLM to decompose questions into searchable triplets with placeholders.
Outcome:	Empirical results show that T2RAG outperforms state-of-the-art multi-round and Graph RAG methods while reducing retrieval costs by up to 45%.

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Xianshu Peng, Wei Wei

Conflict-Aware Soft Prompting for Retrieval-Augmented Generation (2025.emnlp-main)

Challenge:	Existing methods to enhance large models for multi-hop question-answering lack the ability for multipath exploration, strategic look-ahead, stepwise evaluation, and global selection.
Approach:	They propose an algorithm guided by Monte Carlo Tree Search and process rewards that assigns fine-grained rewards to each step in the search path.
Outcome:	The proposed algorithm outperforms various RAG-based methods on four multihop QA datasets and shows that it can self-train and self-update.

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Eunseong Choi, June Park, Hyeri Lee, Jongwuk Lee

Accelerating Adaptive Retrieval Augmented Generation via Instruction-Driven Representation Reduction of Retrieval Overlaps (2025.findings-acl)

Challenge:	Existing studies show that REtrieval-augmented generation (RAG) fails to resolve the conflict between incorrect external context and correct parametric knowledge.
Approach:	They propose a conflict-aware REtrieval-augmented generation system that encodes external context into compact memory embeddings and captures a guidance signal that directs reasoning toward the more reliable knowledge source.
Outcome:	Extensive experiments show that CARE effectively mitigates context-memory conflicts, leading to an average performance gain of 5.0% on QA and fact-checking benchmarks.

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Jie Ou, Jinyu Guo, Shuaihong Jiang, Zhaokun Wang, Libo Qin, Shunyu Yao, Wenhong Tian

Detecting Hallucinations in Retrieval-Augmented Generation via Semantic-level Internal Reasoning Graph (2026.findings-acl)

Challenge:	Existing approaches to augmented generation ignore the overlap in retrieval results . overlapping content is redundantly represented, affecting the overall efficiency.
Approach:	They propose a model-agnostic approach to re-augmented generation that speeds up prefilling and decoding . they propose an instruction-driven module to guide the model to more suitable ways for LLMs .
Outcome:	The proposed approach achieves 2.79 and 2.33 times significant acceleration on average for prefilling and decoding respectively while maintaining equal generation quality.

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Jianpeng Hu, Yanzeng Li, Jialun Zhong, Lei Zou, Wenfa Qi

LLM-Generated Text May Harm Your Retrieval! A Robust Detection Strategy for Retrieval-Augmented Generation (2026.acl-long)

Challenge:	Existing methods for detecting faithfulness hallucinations are coarse or do not capture the models’ internal reasoning processes, making it difficult to learn.
Approach:	They propose a semantic-level internal reasoning graph-based method for detecting faithfulness hallucination using Large language models.
Outcome:	The proposed method achieves better overall performance compared to state-of-the-art baselines on RAGTruth and Dolly-15k.

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Zhaoheng Huang, Yutao Zhu, Ji-Rong Wen, Zhicheng Dou

MM-PoisonRAG: Disrupting Multimodal RAG with Local and Global Knowledge Poisoning Attacks (2026.acl-long)

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.

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Hyeonjeong Ha, Qiusi Zhan, Jeonghwan Kim, Dimitrios Bralios, Saikrishna Sanniboina, Nanyun Peng, Kai-Wei Chang, Daniel Kang, Heng Ji

TopoRAG: Graph-based RAG via Topology-aware Approximate Nearest Neighbor Search (2026.findings-acl)

Challenge:	Existing research exposes multimodal large language models to knowledge poisoning attacks . localized poisoning attack achieves up to 56% success rate even under restricted access . globalized poison attack completely disrupts model generation to 0% accuracy with just one poisoned content.
Approach:	They propose a framework to study the vulnerability of multimodal RAG under knowledge poisoning attacks.
Outcome:	The proposed framework exploits two new attack strategies on multimodal RAGs under knowledge poisoning.

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Tianhao Wu, Siqiang Luo

TH-RAG : Topic-Based Hierarchical Knowledge Graphs for Robust Multi-hop Reasoning in Graph-based RAG Systems (2026.acl-long)

Challenge:	Recent studies extend RAG with graph-structured knowledge, enhancing retrieval to capture relational context beyond isolated text chunks.
Approach:	They propose a retrieval framework that integrates structural constraints into ANN search . they propose heuristic neighbor expansion which augments the retrieved set by traversing immediate neighbors .
Outcome:	The proposed framework improves precision and reduces context redundancy compared to existing methods.

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JungHyoun Kim, Soohyeong Kim, Seok Jun Hwang, Jeonghyeon Park, Yong Suk Choi

Progressive Re-ranking for Multimodal Retrieval-Augmented Generation via Curriculum Learning (2026.findings-acl)

Challenge:	Retrieval-augmented generation (RAG) enables large language models to incorporate external knowledge at inference.
Approach:	They propose a hierarchical framework that organizes triplets into subtopics and topics to enhance connectivity and integrate dispersed information.
Outcome:	Experiments on abstractive and specific QA benchmarks show that TH-RAG outperforms strong baselines in accuracy and robustness while remaining efficient.

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Zhu Min, Yanchao Hao, Jian Liu, Shizhu He, Xi Chen

GRAD: Generalizing RAG Adaptation with Decoding (2026.acl-long)

Challenge:	Existing approaches to improve retrieval performance of large language models are limited by static knowledge.
Approach:	They propose a multimodal re-ranking framework that combines curriculum learning with fine-grained reranking and multimodal section reassessment to improve CLIP-based visual coarse-grain retrieval.
Outcome:	The proposed framework achieves state-of-the-art answer accuracy and competitive retrieval performance on InfoSeek and Enc-VQA.

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Youngwon Lee, Seung-won Hwang, Zhewei Yao, Yuxiong He

BubbleRAG: Interactive Cognitive Offloading with Thought Bubble in Retrieval-Augmented Generation (2026.findings-acl)

Challenge:	Using GRAD, we can steer Retrieval-augmented generation objectives without retraining large language models.
Approach:	They propose an adaptive decoding-time framework that keeps the base generator fixed and composes small, objective-specific guidance at inference.
Outcome:	The proposed framework improves accuracy with favorable latency across public benchmarks and private settings with no in-domain labels while reliably activating helpful objectives and suppressing harmful ones, adaptively to tasks.

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Fuda Ye, Jiachuan Wang, Yongqi Zhang, Lei Chen, Shuangyin Li

MegaRAG: Multimodal Knowledge Graph-Based Retrieval Augmented Generation (2026.acl-long)

Challenge:	Retrieval-augmented generation (RAG) extends the capabilities of large language models (LLMs) by providing access to external knowledge.
Approach:	They propose a framework that emulates human interactive reading through annotation and re-reading by integrating a thought bubble module that offloads internal cognition into external bookmark tokens, which are then annotated back into the context.
Outcome:	The proposed framework offloads internal cognition into external bookmark tokens, which are then annotated back into the context.

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Chi-Hsiang Hsiao, Yi-Cheng Wang, Tzung-Sheng Lin, Yi-Ren Yeh, Chu-song Chen