A Comparison of Independent and Joint Fine-tuning Strategies for Retrieval-Augmented Generation (2025.findings-emnlp)
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| Challenge: | Multiple fine-tuning strategies exist with different costs and benefits for RAG pipelines. |
| Approach: | They evaluate several RAG fine-tuning strategies with different costs and benefits . embedding and generator models can be fine- tuned to increase performance . |
| Outcome: | The proposed techniques improve quality metrics, but have different computational costs. |
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| Challenge: | Large language models (LLMs) encapsulate a vast amount of factual information within their pre-trained weights. |
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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
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| 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. |
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Rafael Teixeira de Lima, Shubham Gupta, Cesar Berrospi Ramis, Lokesh Mishra, Michele Dolfi, Peter Staar, Panagiotis Vagenas
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| Challenge: | Retrieval-augmented generation (RAG) is an approach to augment large language models (LLMs) despite their impressive performance, LLMs can generate plausible sounding but factually incorrect responses (hallucinations) |
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Embedding-Free RAG (2025.findings-emnlp)
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| Challenge: | Retrieval-Augmented Generation (RAG) is the current state-of-the-art method for mitigating the shortcomings of large language models. |
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| Challenge: | Existing approaches to optimize RAG generators fail to align with RAG requirements thoroughly. |
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| Challenge: | Retrieval-Augmented Generation (RAG) systems have several limitations, including noisy or suboptimal retrieval, misuse of retrieval for out-of-scope queries, weak query–document matching, and variability or cost associated with the generator. |
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Does RAG Introduce Unfairness in LLMs? Evaluating Fairness in Retrieval-Augmented Generation Systems (2025.coling-main)
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| Challenge: | Retrieval-Augmented Generation (RAG) models address fairness concerns with respect to sensitive attributes such as gender, geographic location, and other demographic factors. |
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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. |
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