A RAG (Retrieval-Augmented Generation) evaluation dataset built on a fixed knowledge source like Wikipedia can inherit several biases that skew performance metrics. First, coverage bias arises because Wikipedia’s content is unevenly distributed. Topics related to Western culture, technology, or widely recognized historical events are often overrepresented, while niche subjects, non-Western perspectives, or marginalized communities receive less attention. For example, a RAG model tested on queries about African history might underperform if Wikipedia’s coverage of pre-colonial African societies is sparse or Eurocentric. This creates a false impression of the model’s general capabilities. Second, demographic bias emerges from Wikipedia’s contributor base, which skews toward English-speaking, male, and academically trained editors. This can lead to systemic gaps in representing diverse viewpoints, such as gender-neutral language in biographies or balanced coverage of controversial topics. Third, temporal bias occurs because Wikipedia’s snapshot might not reflect recent updates, causing models to fail on time-sensitive queries (e.g., “current COVID-19 variants”) or misrepresent outdated information as current.
To account for these biases, evaluation must prioritize dataset diversification. This involves supplementing test cases with underrepresented topics sourced from alternative repositories (e.g., academic journals, regional encyclopedias) to balance coverage. For instance, adding questions about indigenous knowledge systems or non-English cultural practices can reveal gaps in the model’s reliance on Wikipedia. Additionally, adversarial testing should be used to probe demographic biases: intentionally framing queries to require culturally nuanced answers (e.g., “Explain the causes of the Iran-Iraq War from Iraqi and Iranian perspectives”) helps assess whether the model regurgitates Wikipedia’s dominant narratives or acknowledges limitations. Temporal bias requires time-aware evaluation splits, where questions are categorized by their relevance to the knowledge cutoff date. For example, testing pre-2023 events with a model trained on a 2022 Wikipedia snapshot ensures fair assessment, while post-2023 queries should be excluded or flagged as out-of-scope.
Finally, transparency in reporting is critical. Evaluation results should explicitly note the knowledge source’s limitations, such as stating, “Performance on medical queries reflects Wikipedia’s emphasis on general terminology over specialized research.” Human evaluators with diverse backgrounds can also identify subtle biases missed by automated metrics. For instance, if a RAG model answers a question about “gender roles in society” by citing only Western examples, human judges can flag the omission of global perspectives. By combining these strategies—diversified datasets, adversarial testing, time-aware splits, and human oversight—developers can mitigate biases and ensure evaluations reflect the model’s true capabilities, not just the flaws in its underlying knowledge source.