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In-Context Retrieval-Augmented Language Models
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Retrieval-Augmented Language Modeling (RALM) methods, which condition a language model (LM) on relevant documents from a grounding corpus during generation, were shown to significantly improve language modeling performance. In addition, they can mitigate the problem of factually inaccurate text generation and provide natural source attribution mechanism. Existing RALM approaches focus on modifying the LM architecture in order to facilitate the incorporation of external information, significantly complicating deployment. This paper considers a simple alternative, which we dub In-Context RALM: leaving the LM architecture unchanged and prepending grounding documents to the input, without any further training of the LM. We show that In-Context RALM that builds on off-the-shelf general purpose retrievers provides surprisingly large LM gains across model sizes and diverse corpora. We also demonstrate that the document retrieval and ranking mechanism can be specialized to the RALM setting to further boost performance. We conclude that In-Context RALM has considerable potential to increase the prevalence of LM grounding, particularly in settings where a pretrained LM must be used without modification or even via API access.
Forward citations
Cited by 6 Pith papers
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Shifting from Ranking to Set Selection for Retrieval Augmented Generation
SETR identifies a query's information requirements with chain-of-thought reasoning and selects a compact passage set, improving multi-hop RAG accuracy over fixed-top-k reranking baselines.
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HIRAG: Hierarchical-Thought Instruction-Tuning Retrieval-Augmented Generation
A hierarchical chain-of-thought instruction-tuning curriculum for filtering, combination, and reasoning improves zero-shot retrieval-augmented QA.
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Benchmarking Knowledge-Extraction Attack and Defense on Retrieval-Augmented Generation
A unified benchmark comparing RAG knowledge-extraction attacks and defenses, showing query diversity boosts extraction, embedding attacks fail to transfer, and graph indexing raises per-token leakage.
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On the Merits of LLM-Based Corpus Enrichment
LLM-generated, query-biased documents added to a search corpus improve retrieval effectiveness, RAG answer accuracy, and answer attribution in proof-of-concept experiments that use oracle-selected source documents.
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Structured Relevance Assessment for Robust Retrieval-Augmented Language Models
A retrieval-augmented framework that scores document relevance, balances internal and external knowledge, and abstains when uncertain, claims to cut hallucinations, but the reported evidence is thin and inconsistent.
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