Optimizing Retrieval-Augmented Generation for E-Commerce How-To Assistance (2026.acl-industry)
Copied to clipboard
| Challenge: | Recent advances in large language models (LLMs) have significantly improved Retrieval-Augmented Generation (RAG), enabling assistants that can reliably ground responses in external knowledge sources while maintaining high-quality natural language interaction. |
| Approach: | They propose a RAG-based How-To Assistant that groundes responses in a proprietary knowledge base to provide personalized customer support. |
| Outcome: | The proposed assistant can ground responses in a proprietary knowledge base while maintaining high-quality natural language interaction. |
Similar Papers
AI Knowledge Assist: An Automated Approach for the Creation of Knowledge Bases for Conversational AI Agents (2025.emnlp-industry)
Copied to clipboard
| Challenge: | Existing knowledge base is time-consuming and deters the adoption of conversational AI systems in contact centers. |
| Approach: | They propose a system that extracts knowledge in the form of question-answer (QA) pairs from historical customeragent conversations to automatically build a knowledge base. |
| Outcome: | The proposed system outperforms larger closed-source LLMs on internal data and achieves above 90% accuracy in answering informationseeking questions. |
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. |
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. |
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. |
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. |
Adaptive Retrieval-Augmented Generation for Conversational Systems (2025.findings-naacl)
Copied to clipboard
| Challenge: | Existing studies have shown the effectiveness of retrieving and augmenting external knowledge for informative responses. |
| Approach: | They propose to use a gating model to predict if a conversational system requires retrieval-augmented generation to generate high-quality responses with high confidence. |
| Outcome: | The proposed model can predict if a conversational system requires RAG to generate high-quality responses with high confidence. |
EXPLAIN: Enhancing Retrieval-Augmented Generation with Entity Summary (2025.acl-industry)
Copied to clipboard
Yaozhen Liang, Xiao Liu, Jiajun Yu, Zhouhua Fang, Qunsheng Zou, Linghan Zheng, Yong Li, Zhiwei Liu, Haishuai Wang
| Challenge: | Existing document question answering methods reduce inference costs and input tokens. |
| Approach: | They propose a retrieval-augmented generation method that automatically extracts useful entities and generates summaries from documents. |
| Outcome: | The proposed method surpasses baseline retrieval-augmented generation (RAG) and long-context question answering (LC) methods achieve higher accuracy by processing entire documents, but at the cost of increased computational Corresponding authors. |
HASH-RAG: Bridging Deep Hashing with Retriever for Efficient, Fine Retrieval and Augmented Generation (2025.findings-acl)
Copied to clipboard
| Challenge: | Experimental evaluations on NQ, TriviaQA, and HotpotQA datasets demonstrate that our approach achieves a 90% reduction in retrieval time compared to conventional methods while maintaining considerate recall performance. |
| Approach: | They propose a framework that integrates deep hashing techniques with systematic optimizations to address these limitations. |
| Outcome: | The proposed framework outperforms retrieval/non-retrieval baselines by 1.4-4.3% in EM scores on NQ, TriviaQA, and HotpotQA datasets. |
Open-RAG: Enhanced Retrieval Augmented Reasoning with Open-Source Large Language Models (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods to integrate Large Language Models with external knowledge suffer from limited reasoning capabilities, especially when using open-source LLMs. |
| Approach: | They propose a framework that transforms an arbitrary dense LLM into a parameter-efficient sparse mixture of experts (MoE) model capable of handling complex reasoning tasks. |
| Outcome: | The proposed framework transforms an arbitrary dense LLM into a parameter-efficient sparse mixture of experts (MoE) model capable of handling complex reasoning tasks, including both single- and multi-hop queries. |
Do RAG Systems Cover What Matters? Evaluating and Optimizing Responses with Sub-Question Coverage (2025.naacl-long)
Copied to clipboard
| Challenge: | Existing evaluations of retrieval-augmented generation systems are limited . sub-question coverage measures how well a RAG system addresses different facets of a question. |
| Approach: | They propose a framework for evaluation based on sub-question coverage . they propose to decompose questions into sub-questions and classify them into three types . |
| Outcome: | The proposed evaluation framework measures how well a RAG system addresses different facets of a question. |