Papers with RAG baselines
IAG: Induction-Augmented Generation Framework for Answering Reasoning Questions (2023.emnlp-main)
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| Challenge: | Existing approaches to QA using retrieval-augmented knowledge are limited by limited coverage and noisy information. |
| Approach: | They propose an induction-augmented generation framework that utilizes inductive knowledge along with retrieved documents for implicit reasoning. |
| Outcome: | The proposed framework outperforms RAG and ChatGPT on two Open-Domain QA tasks. |
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 . |
RAG-Instruct: Boosting LLMs with Diverse Retrieval-Augmented Instructions (2025.emnlp-main)
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| Challenge: | Retrieval-Augmented Generation (RAG) has emerged as a key paradigm for enhancing large language models by incorporating external knowledge. |
| Approach: | They propose a method for synthesizing diverse and high-quality RAG instruction data based on any source corpus. |
| Outcome: | The proposed method outperforms existing methods in multiple tasks and achieves strong zero-shot performance. |
Retrieval as Generation: A Unified Framework with Self-Triggered Information Planning (2026.acl-long)
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| Challenge: | Existing models that ground retrieval on external evidence are limited in their ability to implement retrieval-augmented generation. |
| Approach: | They propose a retrieval-augmented generation model that embeds retrieval control directly into generation. |
| Outcome: | The proposed model surpasses strong RAG baselines and uses substantially fewer parameters. |
Candidate-Aware Retrieval and Reranking for Multiple-Choice Question Answering: Arabic as a Case Study (2026.findings-acl)
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| Challenge: | Large language models (LLMs) have recently achieved impressive results on multiple-choice question answering (MCQA) despite advances in English, LLMs continue to underperform in Arabic due to gaps in data coverage, linguistic transfer, and evaluation design. |
| Approach: | They propose a method that jointly models the relevance of both the question and its candidate answers when selecting contextual passages. |
| Outcome: | The proposed approach outperforms standard RAG baselines and reranker baselines while remaining competitive with considerably larger models. |
MASS-RAG: Multi-Agent Synthesis Retrieval-Augmented Generation (2026.findings-acl)
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| Challenge: | Large language models (LLMs) are widely used in retrieval-augmented generation (RAG) when retrieved contexts are noisy, incomplete, or heterogeneous, a single generation process often struggles to reconcile evidence effectively. |
| Approach: | They propose a multi-agent synthesis approach to retrieval-augmented generation that structures evidence processing into multiple role-specialized agents. |
| Outcome: | Experiments on four benchmarks show that MASS-RAG consistently improves performance over strong RAG baselines. |
Investigating Pedagogical Teacher and Student LLM Agents: Genetic Adaptation Meets Retrieval-Augmented Generation Across Learning Styles (2025.emnlp-main)
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Debdeep Sanyal, Agniva Maiti, Umakanta Maharana, Dhruv Kumar, Ankur Mali, C. Lee Giles, Murari Mandal
| Challenge: | Existing models for large language models neglect comprehensive student modeling beyond basic knowledge states and lack mechanisms for teachers to dynamically adapt their approach based on student feedback and collective performance. |
| Approach: | They propose a framework that integrates LLM-based diverse student agents with a self-evolving teacher agent to optimize teacher's pedagogical parameters based on simulated student performance. |
| Outcome: | The proposed framework integrates diverse student agents with a self-evolving teacher agent to optimize teacher pedagogical parameters based on simulated student performance. |
Biomedical Question Answering via Multi-Level Summarization on a Local Knowledge Graph (2026.acl-long)
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| Challenge: | Existing work on how to effectively capture multi-document relationships remains an open question . Existing techniques to mitigate this problem include hierarchical summarization of semantically related chunks or integrating Knowledge Graphs (KGs). |
| Approach: | They propose a method which constructs a local knowledge graph from retrieved documents . they use propositional claims to construct a knowledge graph and contextualize a small language model . |
| Outcome: | The proposed method outperforms RAG on biomedical benchmarks and is generalizable and effective. |
LCR-RAG: Enhancing Logical Consistency in Retrieval-Augmented Generation via Neuro-symbolic Reinforcement Learning (2026.acl-long)
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| Challenge: | Retrieval-Augmented Generation (RAG) is widely used to ground large language models in external knowledge and improve factual accuracy. |
| Approach: | They propose a framework that integrates neuro-symbolic verification with reinforcement learning to optimize logical consistency. |
| Outcome: | The proposed framework outperforms strong RAG baselines on hotpotQA, ASQA, and TriviaQA. |
QQSUM: A Novel Task and Model of Quantitative Query-Focused Summarization for Review-based Product Question Answering (2025.acl-long)
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| Challenge: | Existing review-based product question answering systems generate only a single answer, ignoring the diversity of viewpoints. |
| Approach: | They propose a task which aims to summarize diverse customer opinions into representative Key Points and quantify their prevalence to effectively answer user queries. |
| Outcome: | The proposed task summarizes diverse customer opinions into representative Key Points and quantifies their prevalence to answer user queries. |
RAR2: Retrieval-Augmented Medical Reasoning via Thought-Driven Retrieval (2025.findings-emnlp)
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| Challenge: | Existing methods focus on refining queries without modeling the reasoning process, limiting their ability to retrieve and integrate clinically relevant knowledge. |
| Approach: | They propose a joint learning framework that improves Reasoning-Augmented Retrieval and Retri-Agmented Reasoning. |
| Outcome: | The proposed model outperforms RAG baselines on biomedical question answering datasets. |
RouteRAG: Efficient Retrieval-Augmented Generation from Text and Graph via Reinforcement Learning (2026.findings-acl)
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| Challenge: | Existing graph-based or hybrid systems lack the ability to integrate supplementary evidence as reasoning unfolds. |
| Approach: | They propose a framework that integrates non-parametric knowledge into Large Language Models . they use a RL-based framework to optimize the entire generation process via RL . |
| Outcome: | The proposed framework outperforms existing RAG frameworks in five question answering benchmarks. |
Utility-Oriented Visual Evidence Selection for Multimodal Retrieval-Augmented Generation (2026.acl-long)
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| Challenge: | Existing methods for multimodal retrieval-augmented generation rely on semantic relevance or surface-level similarity, which are often misaligned with the actual utility of visual evidence for downstream reasoning. |
| Approach: | They propose a latent notion of evidence usefulness and propose 'surrogate-accelerated' framework that efficiently estimates evidence utility using lightweight multimodal models. |
| Outcome: | The proposed framework outperforms state-of-the-art models while achieving substantial reductions in computational cost. |
R-Search: Empowering LLM Reasoning with Search via Multi-Reward Reinforcement Learning (2026.findings-acl)
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Qingfei Zhao, Ruobing Wang, Dingling Xu, Daren Zha, Ma Bowen, Zhichun Wang, Shijie Jia, Limin Liu, Xin Wang
| Challenge: | Large language models (LLMs) have demonstrated impressive capabilities in multi-step and long-chain reasoning, but extending their reasoning capabilities to encompass deep interactions with search remains a non-trivial challenge. |
| Approach: | They propose a framework for Reasoning–Search integration that integrates multi-reward signals to optimize the reasoning–search interaction trajectories. |
| Outcome: | Experiments on seven datasets show that R-Search significantly outperforms mainstream RAG baselines. |