Top RAG Papers of the Week (October Week 4, 2024)

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[1] RuleRAG

This paper presents Rule-Guided RAG which explicitly introduces symbolic rules as demonstrations for in-context learning to guide retrievers to retrieve logically related documents. Experiments demonstrate that training-free RuleRAG-ICL effectively improves the retrieval quality and generation accuracy over standard RAG. Moreover, RuleRAG scales well with increasing numbers of retrieved documents and exhibits generalization ability for untrained rules. [Tweet] and [Paper]

[2] ChunkRAG

This paper introduces ChunkRAG, a novel chunk filtering method for RAG systems. Existing RAG methods fail to effectively filter irrelevant chunks which can results in? inaccurate responses.? ChunkRAG? employs semantic chunking to divide documents into coherent sections and utilizes LLM-based relevance scoring to assess each chunk’s alignment with the user’s query. Experiments demonstrated that ChunkRAG outperforms existing RAG models.? [Tweet] and [Paper]

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[3] AutoRAG Framework

This paper introduces the AutoRAG framework, which automatically identifies suitable RAG modules for a given dataset. AutoRAG explores and approximates the optimal combination of RAG modules for the dataset.? AutoRAG is available as a Python library.? [Tweet] and [Paper]

[4] LONG2RAG

Existing RAG benchmarks (1) struggle to evaluate long-context retrieval and (2) lack a? comprehensive evaluation method for assessing LLMs’ ability to generate long-form responses. To address these shortcomings, this paper introduces the LONG2RAG benchmark and the Key Point Recall (KPR) metric. KPR evaluates the extent to which LLMs incorporate key points extracted from the retrieved documents into their generated responses. [Tweet] and [Paper]

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Kalyan KS, Research Scientist(NLP) at Akmmus AI Labs