Why RAG Pipelines Silently Hallucinate — And The Decay Score That Catches It Before The LLM Does
The article discusses the challenges faced by RAG (Retrieval-Augmented Generation) pipelines, particularly the issue of temporal staleness in retrieved documents. It highlights how older documents can lead to hallucinations in language models when they contain outdated information. A proposed solution involves implementing a decay score to assess the freshness of documents before they are processed by the model.
- ▪RAG pipelines can produce inaccurate results when older documents are retrieved alongside newer ones, as they are ranked by semantic similarity.
- ▪The decay score is a metric that indicates the freshness of a document based on its age and the type of source.
- ▪Implementing a decay gate can help filter out outdated information before it enters the language model's context.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 1314572) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } VLSiddarth Posted on May 24 Why RAG Pipelines Silently Hallucinate — And The Decay Score That Catches It Before The LLM Does #rag #python #llm #machinelearning Your RAG pipeline has a blind spot. It is not your embeddings. It is not your retrieval algorithm. It is time. Vector databases return results ranked by semantic similarity. A document from 18 months ago and a document from last week score identically if the wording is similar. The LLM receives both with equal confidence.
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