LONG2RAG: Evaluating Long-Context & Long-Form Retrieval-Augmented Generation with Key Point Recall (2024.findings-emnlp)
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| Challenge: | Retrieval-augmented generation (RAG) is a promising approach to address limitations of fixed knowledge in large language models. |
| Approach: | They propose a benchmark and a metric to assess LLMs' ability to generate long-form responses that exploit retrieved information. |
| Outcome: | The proposed benchmarks lack a comprehensive evaluation method to assess LLMs' ability to generate long-form responses that effectively exploits retrieved information. |
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| Challenge: | Recent LLMs like Gemini-1.5 and GPT-4 show exceptional capabilities to understand long contexts directly. |
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Dongyang Li, Junbing Yan, Taolin Zhang, Chengyu Wang, Xiaofeng He, Longtao Huang, Hui Xue’, Jun Huang
| Challenge: | Existing RAG methods focus on improving the task performance, without fine-grained process of knowledge. |
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| Challenge: | Existing benchmarks for long-context RAG focus primarily on English . low-resource languages lack comprehensive evaluation frameworks limiting their progress in retrieval-based tasks. |
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Rafael Teixeira de Lima, Shubham Gupta, Cesar Berrospi Ramis, Lokesh Mishra, Michele Dolfi, Peter Staar, Panagiotis Vagenas
| Challenge: | Retrieval Augmented Generation (RAG) systems are widespread in the industry. |
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MS-RAG: Simple and Effective Multi-Semantic Retrieval-Augmented Generation (2025.emnlp-main)
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| Challenge: | Existing methods for large language models suffer from poor indexing and inference speed . graph-based RAGs heavily rely on LLM for retrieval thus inference slow . |
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Open-RAG: Enhanced Retrieval Augmented Reasoning with Open-Source Large Language Models (2024.findings-emnlp)
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| Challenge: | Existing methods to integrate Large Language Models with external knowledge suffer from limited reasoning capabilities, especially when using open-source LLMs. |
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