AI Insight
This study introduces SKILL-RAG, a new approach to improve Retrieval-Augmented Generation systems by leveraging a language model's "self-knowledge" - understanding what it knows and doesn't know - to filter retrieved documents. Using reinforcement learning, the method identifies and removes irrelevant content at the sentence level before incorporating external information into the model. Testing on Llama2-7B and Qwen3-8B models across multiple question-answering benchmarks showed improved answer quality while reducing the number of input documents needed.
Why it matters
This addresses a critical problem in AI systems that combine their internal knowledge with external data sources: reducing hallucinations caused by incorporating irrelevant retrieved information. The approach could make AI assistants and search systems more reliable by helping them better distinguish between useful and unhelpful external information when answering queries.
Understand the Science
⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
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Abstract: Retrieval-Augmented Generation (RAG) has significantly improved the performance of large language models (LLMs) on knowledge-intensive tasks in recent years. However, since retrieval systems may return irrelevant content, incorporating such information into the model often leads to hallucinations. Thus, identifying and filtering out unhelpful retrieved content is a key challenge for improving RAG performance.To better integrate the internal knowledge of the model with external knowledge from retrieval, it is essential to understand what the model “knows” and “does not know” (which is also called “self-knowledge”). Based on this insight, we propose SKILL-RAG (Self-Knowledge Induced Learning and Filtering for RAG), a novel method that leverages the model’s self-knowledge to determine which retrieved documents are beneficial for answering a given query. We design a reinforcement learning-based training framework to explicitly elicit self-knowledge from the model and employs sentence-level granularity to filter out irrelevant content while preserving useful knowledge.We evaluate SKILL-RAG using Llama2-7B and Qwen3-8B on several question answering benchmarks. Experimental results demonstrate that SKILL-RAG not only improves generation quality but also significantly reduces the number of input documents, validating the importance of self-knowledge in guiding the selection of high-quality retrievals.
Source: SKILL-RAG: Self-Knowledge Induced Learning and Filtering for Retrieval-Augmented Generation