Withdrawn Paper Proposes Self-Knowledge Method to Filter Noisy Retrieved Content in RAG Systems

A paper submitted to the arXiv preprint server has been withdrawn, but outlined a potential solution to a persistent problem in retrieval-augmented generation (RAG): distinguishing useful retrieved documents from irrelevant noise that can trigger inaccurate outputs.

The Core Problem#

Retrieval-augmented generation has become a valuable technique for enhancing large language model performance on knowledge-heavy tasks. However, the approach carries a significant risk—when retrieval systems surface irrelevant or contradictory content, incorporating it into the generation process often produces hallucinations or factually incorrect responses. Filtering out these unhelpful documents before they reach the model remains a key technical challenge.

The Proposed Approach#

The withdrawn work, titled SKILL-RAG, introduced the concept of self-knowledge as a foundation for document filtering. The basic premise is that models should be evaluated on what they already understand and what they lack; this understanding can then guide decisions about which retrieved materials are genuinely beneficial.

The method uses a reinforcement learning training framework to explicitly extract this self-knowledge from the model. Rather than filtering at the document level, it operates at sentence-level granularity, selectively removing irrelevant passages while retaining useful context.

Experimental Results#

The authors tested SKILL-RAG with Llama2-7B and Qwen3-8B models across several question-answering benchmarks. Their findings indicated that the approach improved generation quality while substantially reducing the number of input documents the model had to process—demonstrating that understanding what a model already knows can be instrumental in selecting higher-quality retrievals.

The paper has since been withdrawn by its author, Tomoaki Isoda.