When a retrieval strategy returns contradictory information, the LLM must navigate conflicting claims without amplifying misinformation. For example, if one source states "Event X occurred in 1995" and another claims "Event X occurred in 2000," the model should avoid asserting either date as definitive unless it can resolve the conflict. The LLM’s primary responsibility is to acknowledge the discrepancy, provide context about the sources (e.g., recency, reliability), and present the information neutrally. For instance, it might respond, "Sources differ on the date: Source A (a peer-reviewed journal) states 1995, while Source B (a recent news article) cites 2000. Further verification may be needed." This approach maintains transparency and prevents the model from overcommitting to unverified claims.
To handle contradictions effectively, the LLM should prioritize strategies like source credibility assessment, consensus detection, and uncertainty signaling. For example, if three reputable sources agree on a fact while one outlier disagrees, the model might note the majority view while flagging the outlier. If no consensus exists, the response should explicitly state the conflict and avoid taking sides. Technical methods like confidence scoring (assigning weights to sources based on metadata like publication date or domain authority) can help automate this process. Additionally, the model could prompt users to clarify or refine their query if ambiguity contributes to the conflict (e.g., "Are you referring to the initial discovery or a later revision?"). The goal is to balance accuracy with humility, ensuring users understand the limitations of the information.
Evaluating whether the LLM handled contradictions correctly involves both automated metrics and human judgment. Automated tests might measure how often the model (1) acknowledges conflicts explicitly, (2) cites sources accurately, and (3) avoids presenting disputed claims as facts. For example, benchmarks like TruthfulQA or custom datasets with injected contradictions can quantify performance. Human evaluators should assess whether responses are transparent about uncertainty, fairly represent conflicting evidence, and guide users toward resolution (e.g., suggesting authoritative sources). User feedback mechanisms, such as allowing users to flag misleading or confusing answers, also provide real-world validation. A well-handled contradiction leaves the user informed about the conflict rather than misled by it.