| Challenge: | Existing retrieval methods prioritize relevance without ensuring the retrieved documents semantically support answering the queries. |
| Approach: | They propose a novel approach to improve Textual Entailment Retrieval within the framework of Retri-Augmented Generation (RAG) they transform query embeddings to better align with semantic entailment without re-encoding the document corpus. |
| Outcome: | The proposed approach consistently approaches the skyline across multiple datasets, demonstrating its strength in many-to-many retrieval scenarios. |
Similar Papers
Enhancing Retrieval-Augmented Generation: A Study of Best Practices (2025.coling-main)
Copied to clipboard
| Challenge: | Retrieval-augmented generation systems have shown remarkable advancements by integrating retrieval mechanisms into language models, enhancing their ability to produce more accurate and contextually relevant responses. |
| Approach: | They propose to integrate query expansion, various novel retrieval strategies, and a Contrastive In-Context Learning RAG to improve response quality. |
| Outcome: | The proposed RAGs incorporate query expansion, various novel retrieval strategies, and a novel Contrastive In-Context Learning RAG. |
Searching for Best Practices in Retrieval-Augmented Generation (2024.emnlp-main)
Copied to clipboard
Xiaohua Wang, Zhenghua Wang, Xuan Gao, Feiran Zhang, Yixin Wu, Zhibo Xu, Tianyuan Shi, Zhengyuan Wang, Shizheng Li, Qi Qian, Ruicheng Yin, Changze Lv, Xiaoqing Zheng, Xuanjing Huang
| Challenge: | Retrieval-augmented generation (RAG) techniques have proven to be effective in integrating up-to-date information, mitigating hallucinations, and enhancing response quality, especially in specialized domains. |
| Approach: | They propose several strategies for deploying RAG that balance performance and efficiency. |
| Outcome: | The proposed approaches can significantly enhance question-answering capabilities and accelerate the generation of multimodal content using a “retrieval as generation” strategy. |
Question Decomposition for Retrieval-Augmented Generation (2025.acl-srw)
Copied to clipboard
| 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 . |
Retrieval-augmented Generation across Heterogeneous Knowledge (2022.naacl-srw)
Copied to clipboard
| Challenge: | Existing methods for retrieving knowledge from a single source homogeneous corpus have been gaining increasing attention in the field of natural language processing (NLP) however, they still suffer from the following drawbacks: (i) They are usually trained offline, making the model agnostic to the latest information, e.g., asking a chat-bot about COVID-19. |
| Approach: | They propose to use a single-source homogeneous corpus to generate retrieval-augmented generation models that can learn from the pre-training corpus. |
| Outcome: | The proposed methods have been applied to various knowledge-intensive NLP tasks, but most of the work has focused on retrieving unstructured text documents from Wikipedia. |
Uplift-RAG: Uplift-Driven Knowledge Preference Alignment for Retrieval-Augmented Generation (2025.findings-emnlp)
Copied to clipboard
| 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. |
Retrieval Enhancements for RAG: Insights from a Deployed Customer Support Chatbot (2026.eacl-industry)
Copied to clipboard
Daniel González Juclà, Mohit Tuteja, Marcos Esteve Casademunt, Keshav Unnikrishnan, Yasir Usmani, Arvind Roshaan
| Challenge: | a persistent gap remains between Recall@10 and Recall @50 across datasets . |
| Approach: | They evaluate embedding model comparison, Reciprocal Rank Fusion and embedded concatenation techniques to improve retrieval quality. |
| Outcome: | The proposed methods outperform traditional cross-encoders in identifying high-relevance passages. |
Efficient Dynamic Clustering-Based Document Compression for Retrieval-Augmented-Generation (2025.findings-emnlp)
Copied to clipboard
| Challenge: | RAG implementations face challenges in addressing retrieved noise and redundant content . current RAG methods lack the ability to exploit fine-grained inter-document relationships . |
| Approach: | They propose a retrieval-augmented generation framework that exploits latent inter-document relationships while removing irrelevant information and redundant content. |
| Outcome: | The proposed framework achieves consistent performance improvements on knowledge-QA and hallucination-Detection datasets. |
Inference Scaling for Bridging Retrieval and Augmented Generation (2025.findings-naacl)
Copied to clipboard
| 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. |
Embedding-Free RAG (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Retrieval-Augmented Generation (RAG) is the current state-of-the-art method for mitigating the shortcomings of large language models. |
| Approach: | They propose a model-agnostic approach to retrieval-augmented generation that leverages generalized reasoning abilities of large language models. |
| Outcome: | Embedding-free RAG outperforms existing state-of-the-art methods in a wide range of domains. |
Query Decomposition for RAG: Balancing Exploration-Exploitation (2026.eacl-long)
Copied to clipboard
Roxana Petcu, Kenton Murray, Daniel Khashabi, Evangelos Kanoulas, Maarten de Rijke, Dawn Lawrie, Kevin Duh
| 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. |