What biases might be present in a RAG evaluation dataset if the knowledge source is fixed (e.g., Wikipedia) and how do we account for those in judging performance?

A RAG (Retrieval-Augmented Generation) evaluation dataset that relies on a fixed knowledge source like Wikipedia may contain biases stemming from the source itself. First, coverage bias occurs because Wikipedia’s content is unevenly distributed. Topics popular in Western, educated, or technical contexts are often overrepresented, while marginalized cultures, niche subjects, or less-documented events may lack depth. For example, a RAG system might answer well on “Newtonian physics” but fail on “traditional Māori navigation techniques” due to sparse source material. Second, recency bias arises because Wikipedia’s updates lag behind real-world events. A dataset built from a static snapshot will struggle with queries about recent developments, like a new scientific discovery or a geopolitical event. Third, systemic bias reflects Wikipedia’s editorial practices, which can skew toward majority viewpoints. Articles about controversial topics (e.g., climate change) may prioritize certain perspectives, leading the model to reproduce those biases in responses.

To account for these biases, developers should design evaluation benchmarks that explicitly test gaps in the knowledge source. For coverage bias, include queries spanning underrepresented topics and verify if the model acknowledges knowledge limits instead of hallucinating. For instance, if evaluating a medical RAG system, include rare diseases absent from Wikipedia to test how it handles missing data. For recency bias, curate time-sensitive questions and measure whether the system either provides outdated answers or correctly states the information is unavailable. To address systemic bias, use adversarial test cases that probe controversial or culturally sensitive topics. For example, ask about competing historical narratives (e.g., “Causes of the Iraq War”) and check if responses reflect balanced sourcing or default to dominant viewpoints.

Finally, performance metrics must go beyond accuracy to include robustness checks. Use precision (how often retrieved facts are correct) and coverage-awareness (tracking when the system admits ignorance) alongside human evaluation. For example, if the model answers a question about a 2023 event using a 2020 Wikipedia snapshot, penalize confident but incorrect replies. Additionally, augment the evaluation with external sources to verify answers when possible. Developers can also bias-mitigation techniques like data augmentation (adding synthetic underrepresented queries) or fine-tuning the model to recognize and flag uncertain outputs. By explicitly mapping the fixed source’s limitations and testing against them, evaluations can more fairly assess a RAG system’s true capabilities.