Challenge: Existing studies have focused on human-annotated search queries but they can not cover conversations of various domains.
Approach: They propose a domain adaptation framework that uses retrieval-augmented generation to improve the model's robustness.
Outcome: The proposed model is more robust and performs significantly better in a more challenging setting over strong baselines.

Similar Papers

Adaptive Retrieval-Augmented Generation for Conversational Systems (2025.findings-naacl)

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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.
Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answering (2023.tacl-1)

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Challenge: Retrieval Augment Generation (RAG) has been developed for use in open-domain question answering (ODQA) but it is not optimized for use with other specialized domains such as healthcare and news.
Approach: They propose an extension to RAG that can adapt to a domain-specific knowledge base by updating all components of the external knowledge base during training.
Outcome: The proposed extension can adapt to a domain-specific knowledge base by updating all components of the external knowledge base during training.
Comprehensive Comparison of RAG Methods Across Multi-Domain Conversational QA (2026.eacl-srw)

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Challenge: Existing studies evaluate RAG methods in isolation and focus on single-turn settings.
Approach: They compare retrieval-augmented generation methods for multi-turn conversational QA with those that use dialogue history and coreference to ground large language models.
Outcome: The proposed methods outperform vanilla RAG and advanced methods fail to yield gains and can even degrade performance below the No-RAG baseline.
RAG-Studio: Towards In-Domain Adaptation of Retrieval Augmented Generation Through Self-Alignment (2024.findings-emnlp)

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Challenge: Existing RAG systems that use pre-trained LLMs and retrievers often fail in specialized domains and applications.
Approach: They propose a self-aligned training framework that adapts general RAG models to specific domains solely through synthetic data.
Outcome: Experiments on specialized domain corpus, general LLM, and general retriever show that the self-aligned training framework outperforms human-annotated training data in specialized fields.
RE-RAG: Improving Open-Domain QA Performance and Interpretability with Relevance Estimator in Retrieval-Augmented Generation (2024.emnlp-main)

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

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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%.
Test-time Corpus Feedback: From Retrieval to RAG (2026.findings-eacl)

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Challenge: Retrieval-augmented generation (RAG) pipelines treat retrieval and reasoning as isolated components, limiting performance on complex tasks.
Approach: They propose to integrate large language models with retrieval to improve query quality . they also propose to use feedback to improve the query, retrieved context, or document pool .
Outcome: The proposed methods bridge IR and NLP perspectives and highlight retrieval as a dynamic, learnable component of end-to-end RAG systems.
RAC: Retrieval-augmented Conversation Dataset for Open-domain Question Answering in Conversational Settings (2024.emnlp-industry)

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Challenge: Existing studies constrain questions and answers within predefined contexts, excluding the retrieval process.
Approach: They present a retrieval-augmented conversation dataset that addresses key challenges . they propose a system that combines query rewriting and retrieval with reranking .
Outcome: The proposed system improves query rewriting, retrieval, reranking, and response generation performance.
Domain-Specific Data Generation Framework for RAG Adaptation (2026.findings-acl)

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Challenge: Retrieval-Augmented Generation (RAG) combines the language understanding and reasoning capabilities of large language models (LLMs) with external retrieval to produce domain-grounded responses.
Approach: They propose a scalable and modular data-centric framework for generating domain-grounded question–answer–context triples tailored to diverse RAG adaptation strategies.
Outcome: The proposed framework generates domain-grounded question–answer–context triples for multiple RAG adaptation strategies.
A Survey of RAG-Reasoning Systems in Large Language Models (2025.findings-emnlp)

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Challenge: a survey of RAG-based reasoning-based approaches shows that it is not effective for multi-step inferences.
Approach: They map how advanced reasoning optimizes each stage of RAG . they show how retrieved knowledge supply missing premises and expand context for complex inference .
Outcome: The proposed frameworks achieve state-of-the-art across knowledge-intensive benchmarks.

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