Q-PRM: Adaptive Query Rewriting for Retrieval-Augmented Generation via Step-level Process Supervision (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to rewriting queries often lack supervision signals for intermediate steps . existing approaches rely on outcome-supervised training or heuristic rules to guide the rewrite process . |
| Approach: | They propose a query rewriting framework that generates process-level supervision signals for intermediate steps. |
| Outcome: | a new query rewriting framework outperforms existing approaches on open-domain QA benchmarks. |
Similar Papers
Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity (2024.naacl-long)
Copied to clipboard
| Challenge: | Recent Large Language Models (LLMs) generate factually incorrect answers based on their parametric memory. |
| Approach: | They propose a retrieval-augmented large language model that can dynamically select the most suitable strategy based on query complexity. |
| Outcome: | The proposed approach improves the performance of QA systems on open-domain QA datasets. |
Align Documents to Questions: Question-Oriented Document Rewriting for Retrieval-Augmented Generation (2026.findings-acl)
Copied to clipboard
| Challenge: | Retrieval-Augmented Generation (RAG) enhances the factuality of Large Language Models (LLMs) however, LLMs exhibit a stylistic bias when presented with mixed contexts, revealing a bottleneck in their utility. |
| Approach: | They propose a style-controlled rewriter that aligns retrieved documents with a question-oriented style while preserving facts. |
| Outcome: | The proposed model improves RAG pipelines by 8% with negligible latency overhead. |
RE-RAG: Improving Open-Domain QA Performance and Interpretability with Relevance Estimator in Retrieval-Augmented Generation (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to retrieval augmented generation (RAG) are based on parametric knowledge and external knowledge. |
| Approach: | They propose a weakly supervised method for training a relevance estimator (RE) that provides relative relevance between contexts as previous rerankers did, and provides confidence, which can be used to classify whether given context is useful for answering the given question. |
| Outcome: | The proposed framework improves previously unreferenced large language models and can be trained with a small generator without labels for correct contexts. |
DecEx-RAG: Boosting Agentic Retrieval-Augmented Generation with Decision and Execution Optimization via Process Supervision (2025.emnlp-industry)
Copied to clipboard
Yongqi Leng, Yikun Lei, Xikai Liu, Meizhi Zhong, Bojian Xiong, Yurong Zhang, Yan Gao, null Yiwu, Yao Hu, Deyi Xiong
| Challenge: | Recent advances in outcome-supervised reinforcement learning (RL) have shown strong performance, but this approach still suffers from inefficient exploration, sparse reward signals, and ambiguous global reward feedback. |
| Approach: | They propose a model that models RAG as a Markov Decision Process (MDP) and introduces an efficient pruning strategy to optimize data expansion. |
| Outcome: | The proposed model outperforms existing methods and achieves an average performance improvement of 6.2% across six datasets. |
CtrlA: Adaptive Retrieval-Augmented Generation via Inherent Control (2025.findings-acl)
Copied to clipboard
Liu Huanshuo, Hao Zhang, Zhijiang Guo, Jing Wang, Kuicai Dong, Xiangyang Li, Yi Quan Lee, Cong Zhang, Yong Liu
| 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. |
MiniELM: A Lightweight and Adaptive Query Rewriting Framework for E-Commerce Search Optimization (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for rewriting query terms struggle with natural language understanding . generative methods face high inference latency and cost in offline settings . |
| Approach: | They propose a hybrid pipeline for rewriting query queries using offline knowledge distillation and online reinforcement learning. |
| Outcome: | The proposed pipeline improves query relevance, diversity, adaptability and cost-effective evaluation without manual annotations on Amazon ESCI dataset. |
TC–RAG: Turing–Complete RAG’s Case study on Medical LLM Systems (2025.acl-long)
Copied to clipboard
Xinke Jiang, Yue Fang, Rihong Qiu, Haoyu Zhang, Yongxin Xu, Hao Chen, Wentao Zhang, Ruizhe Zhang, Yuchen Fang, Xinyu Ma, Xu Chu, Junfeng Zhao, Yasha Wang
| Challenge: | Existing approaches to RAG neglect system state variables, resulting in poor performance and erroneous knowledge accumulation. |
| Approach: | They propose a framework that incorporates a Turing Complete System to manage state variables and manage retrieval halting. |
| Outcome: | The proposed framework improves on seven real-world healthcare datasets and shows that it is more accurate than existing methods. |
Query Rewriting in Retrieval-Augmented Large Language Models (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies focus on adapting either the retriever or the reader, but this approach is more focused on adaptation of the query itself. |
| Approach: | They propose a new framework for retrieval-augmented Large Language Models . they propose rewrite-retrieve-read instead of retrieve-then-read . |
| Outcome: | The proposed framework improves performance on downstream tasks, open-domain QA and multiple-choice QA. |
Learning to Explore and Select for Coverage-Conditioned Retrieval-Augmented Generation (2025.findings-naacl)
Copied to clipboard
| Challenge: | Recent advances of large language models (LLMs) have enabled them to provide long and detailed responses by leveraging their parametric knowledge. |
| Approach: | They introduce a dataset of 10K hierarchical sets of information-seeking subqueries that define structured boundaries for outline creation and evaluation in C2 scenarios. |
| Outcome: | The proposed model generates higher-quality outlines that better fulfill diverse user needs when trained with alignment techniques like DPO. |
Reasoning with Memory: Adaptive Information Management for Retrieval-Augmented Generation (2026.findings-acl)
Copied to clipboard
| Challenge: | Multi-hop reasoning remains a fundamental challenge for Retrieval-Augmented Generation systems. |
| Approach: | They propose a framework that provides a dynamic cognitive workspace for multi-hop reasoning . it uses an explicit working memory that persists across retrieval cycles and is continuously updated . |
| Outcome: | The proposed framework achieves state-of-the-art performance over existing systems on eight QA benchmarks. |