A high-performing retriever can still lead to hallucinations in an LLM due to the model’s inherent limitations in context utilization, generation behavior, and training biases. Even with accurate retrieved information, the LLM might prioritize fluency over factual correctness, misinterpret ambiguous details, or rely on outdated or incorrect internal knowledge.
First, LLMs are trained to generate coherent text, not to verify facts. When generating responses, they optimize for linguistic patterns rather than truthfulness. For example, if the retrieved context contains nuanced or conflicting information (e.g., conflicting study results about a medical treatment), the LLM might oversimplify or blend details to produce a fluent but incorrect answer. This is exacerbated in open-ended queries where the model defaults to filling gaps with assumptions from its training data. For instance, if the retriever provides a document stating "Study X found Drug A reduces symptoms," but omits that this only applies to a specific population, the LLM might generalize the claim incorrectly.
Second, the LLM’s architecture can misinterpret or ignore retrieved context. Attention mechanisms may fail to prioritize key details, especially in lengthy or complex passages. For example, in a question about climate change, if the retriever returns a dense report with both historical data and speculative future scenarios, the model might conflate projections with established facts. Similarly, if the context requires logical reasoning (e.g., "Event B occurred after Event A due to X"), the LLM might misattribute causality if the connection isn’t explicitly stated. This is common in technical domains where precise relationships (e.g., "correlation vs. causation") are critical but not explicitly highlighted in the retrieved text.
Finally, training biases and outdated knowledge play a role. LLMs often rely on patterns from their training data, which might conflict with up-to-date retrieved information. For example, a retriever might fetch a 2023 study contradicting a widely accepted 2018 finding, but the LLM’s internal knowledge (based on pre-2021 data) might override the newer context. Similarly, if the retrieved content includes jargon or domain-specific terms the model isn’t familiar with (e.g., a niche engineering concept), it might generate plausible-sounding but incorrect explanations. This is especially problematic when the model lacks explicit instructions to defer to the provided context, leading it to "guess" instead of strictly adhering to the retrieved information.
In summary, hallucinations arise from the LLM’s design trade-offs (fluency over accuracy), context misinterpretation, and conflicts between retrieved data and pre-existing knowledge. Mitigation requires better prompting (e.g., "Answer only using the provided context"), fine-tuning to prioritize external data, and improving the model’s ability to handle ambiguity.