When a retrieval strategy returns contradictory information, the LLM must first acknowledge the conflict and avoid presenting conflicting claims as equally valid without context. The model should prioritize identifying discrepancies, assess source reliability, and provide a balanced explanation that highlights the disagreement. For example, if one source claims “Python is faster for data processing” and another states “Java is faster,” the LLM should note both claims, clarify the context (e.g., specific use cases or benchmarks), and avoid asserting a definitive answer unless evidence strongly supports one side. This requires the model to analyze factors like publication date, author expertise, or consensus among reputable sources.
To handle contradictions effectively, the LLM should follow a structured approach. First, it should flag the conflict explicitly (e.g., “Sources disagree on this point”). Next, it should compare metadata like source credibility—for instance, prioritizing peer-reviewed studies over forum posts. If no clear consensus exists, the model should explain the reasoning behind differing viewpoints. For example, in a medical query where one study links a food to cancer risk and another disproves it, the LLM might say, “Older studies suggested a risk, but recent large-scale trials found no evidence. Debate continues due to limitations in early research.” Additionally, the model should avoid amplifying fringe opinions unless explicitly asked, ensuring answers reflect mainstream or well-supported perspectives.
Evaluating whether the LLM handled contradictions correctly involves both automated and human checks. Automated tests could use predefined scenarios with known conflicts (e.g., “Source A says X; Source B says Y”) to verify if the model (1) detects the conflict, (2) cites sources accurately, and (3) provides context for the disagreement. For instance, a test case might check if the model mentions conflicting climate change studies while emphasizing the scientific consensus. Human evaluators can then assess the clarity, fairness, and logical coherence of responses. Metrics might include precision (did the model correctly identify all conflicting claims?), neutrality (did it avoid undue bias?), and usefulness (did the explanation help users make informed decisions?). Regular audits using real-world examples, like contradictory API documentation, can further refine the model’s handling of ambiguities.
