Understanding RAG Part VIII: Mitigating Hallucinations in RAG
Image by Editor | Midjourney & Canva
Be sure to check out the previous articles in this series:
Among the different kinds of issues and challenges that can hinder language model performance, hallucinations are frequently at the top of the list. Hallucinations occur when language models generate false, misleading, or nonsensical information. In retrieval-augmented generation (RAG) systems, where language models are enhanced by retrieving and incorporating external information to improve factual grounding, this problem is mitigated but not eliminated.
In this new installment of our Understanding RAG article series, we will examine the problem of hallucinations, how they manifest in RAG systems compared to standalone language models, and most importantly, how to navigate this challenging issue.
Why Hallucinations Still Occur in Hallucinations in RAG Systems
While it holds — as discussed in the initial articles in this series — that one of the main advantages of RAG systems compared to conventional language models is their ability to reduce hallucinations by retrieving and incorporating factually accurate information, hallucinations can still arise for multiple reasons. In fact, they remain one of the most significant challenges in RAG systems, particularly those approaches whose retrieval accuracy is limited or whose knowledge base quality is also not sufficient.
One reason hallucinations may still occur in RAG systems is quite simple: if retrieved data contains errors, the generated response may also be incorrect or misleading. In a RAG system that relies on a knowledge base (typically a corpus of documents, but sometimes structured databases as well) where data is introduced by humans, collected by sensors, etc., it is not uncommon to have entries that contain errors or inaccuracies. When the RAG’s retriever prioritizes or misinterprets these “corrupted” data entries or documents, the risk of hallucination increases. An incorrectly introduced person name in a document or database is more than enough to trigger the “hallucination show” if the user makes a query related that person to a RAG system… or even worse if the user is that person itself!
Another issue arises when retrieved information lacks sufficient detail or contextual nuances that are crucial for proper interpretation. For instance, the retriever behind a banking chatbot’s RAG system may retrieve information about mortgage conditions in response to a customer’s inquiry. However, if the customer has a disability or special status that qualifies them for additional benefits, and the retriever fails to retrieve this specific information, the generated response might omit a crucial opportunity for the customer. This not only leads to misinformation but can also result in a poor user experience, potentially driving the customer to a competitor.
In either case, the generator (the language model within the RAG system) attempts to construct a response based on incomplete or misleading data, leading to an unreliable or inaccurate output.
Mitigating Hallucinations in RAG Systems
In broad terms, we can identify and categorize three types of strategies or focus points to mitigate hallucinations in RAG systems: data, context, and AI & reasoning processes within the retriever and generator.
Data-Related Mitigation Strategies
The key to reducing hallucinations lies in ensuring high-quality, well-curated data within the knowledge base(s) used by the retriever. If the retrieved documents contain errors, imprecise entries, outdated information, or biases, the generator may articulate misleading or incorrect responses. To improve reliability, strategies include rigorous data curation, having the knowledge base continuously and systematically updated, automated fact-checking approaches, and filtering out low-quality or irrelevant sources.
Context-Related Mitigation Strategies
Even when the quality and accuracy of the data have been improved, hallucinations may still arise if the model fails to fully capture the user’s intent or retrieve all relevant information. To address this, strategies focused on context improvement include optimizing retrieval techniques, using query expansion for refined searches, applying reranking models to prioritize the most relevant documents to the particular scenario, and employing advanced prompt engineering techniques. These are strategies that may help improve the relevance of retrieved information and context so that the final prompt passed to the generator provides a solid contextual foundation.
AI & Reasoning Process-Related Mitigation Strategies
Finally, even with a well-structured context and high-quality data, the reasoning process carried out by the language model can still lead to hallucinations. To navigate this final challenge, common strategies include fine-tuning models with instruction-following datasets (collections of training instances designed to help language models comprehend and follow explicit instructions), incorporating logical reasoning and common-sense reasoning techniques, leveraging external verification tools like fact-checking APIs, and integrating multi-step reasoning frameworks in the RAG workflow to yield more coherent and precise responses.
| Mitigation | Primary Focus | Key Strategies & Techniques | Benefits/Outcome |
|---|---|---|---|
| Data | Curate and maintain high-quality data | Rigorous curation, continuous updates, automated fact-checking, filtering of low-quality sources | Reduces errors from outdated or inaccurate information; improves factual grounding |
| Context | Capture user intent & enhance retrieval detail | Optimized retrieval methods, query expansion, reranking models, advanced prompt engineering | Improves relevance and completeness of retrieved information |
| AI & Reasoning | Refine model decision-making & reasoning | Fine-tuning with instruction datasets, logical and common-sense reasoning, multi-step frameworks, external verification tools | Mitigates inherent model hallucinations, leading to more coherent responses |
Wrapping Up
Hallucinations are a key problem in nowadays AI systems based on language models, with RAG systems being no exception despite their ability to partly address this issue. This article discussed the hallucination problem in the context of RAG, highlighting reasons why this problem may still occur in systems that retrieve external information before generating a response, and identifying several types of practical strategies that can be implemented in RAG systems, databases, and knowledge bases to mitigate them.