mlsanityData to 7 Oct 2026

K-COMP: Retrieval-Augmented Medical Domain Question Answering With Knowledge-Injected Compressor

arXiv 2501.13567 ↗First posted 23 Jan 2025cs.CL cs.AIcited by 2

On the map: Retrieval-augmented generation → Medical and biomedical RAG

Abstract (arXiv)

Retrieval-augmented question answering (QA) integrates external information and thereby increases the QA accuracy of reader models that lack domain knowledge. However, documents retrieved for closed domains require high expertise, so the reader model may have difficulty fully comprehending the text. Moreover, the retrieved documents contain thousands of tokens, some unrelated to the question. As a result, the documents include some inaccurate information, which could lead the reader model to mistrust the passages and could result in hallucinations. To solve these problems, we propose K-comp (Knowledge-injected compressor) which provides the knowledge required to answer correctly. The compressor automatically generates the prior knowledge necessary to facilitate the answer process prior to compression of the retrieved passages. Subsequently, the passages are compressed autoregressively, with the generated knowledge being integrated into the compression process. This process ensures alignment between the question intent and the compressed context. By augmenting this prior knowledge and concise context, the reader models are guided toward relevant answers and trust the context.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Medical and biomedical RAG, one of 15 directions in Retrieval-augmented generation. Builds on 54 of its 74 references that are in the map. First posted on arXiv 23 Jan 2025.

Builds on

54 of 74 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024Benchmarking Retrieval-Augmented Generation for Medicine20 Feb

Other references · 53

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38 more
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Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025Injecting Domain-Specific Knowledge into Large Language Models: A Comprehensive Surveycited by 47
  2. 2025MedCoT-RAG: Causal Chain-of-Thought RAG for Medical Question Answeringcited by 5

Similar, not cited

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  5. 2022Augmenting Pre-trained Language Models with QA-Memory for Open-Domain Question Answering
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  8. 2025KERAG: Knowledge-Enhanced Retrieval-Augmented Generation for Advanced Question Answering