Monday 14 July 2025
The internet has revolutionized the way we access information, making it easier than ever to find answers to our questions. But have you ever stopped to think about what happens when those sources of information don’t agree? A new study sheds light on a common problem that can affect even the most advanced language models: conflicting knowledge.
When we search for information online, we’re often presented with multiple sources that claim to have the answer. But what if those sources disagree? This is known as a knowledge conflict, and it’s more common than you might think. In fact, researchers found that up to 40% of searches may involve conflicting information.
So how do language models, like those used in chatbots and virtual assistants, handle this kind of situation? Unfortunately, they often struggle to resolve the conflict and provide a clear answer. This can lead to confusion and frustration for users who are relying on these models to get accurate information.
The researchers behind the study developed a new taxonomy of knowledge conflicts, which categorizes them into five different types. These include no conflict, where all sources agree; complementary information, where multiple perspectives are valid but don’t contradict each other; conflicting opinions or research outcomes, where genuinely opposing viewpoints are presented; conflict due to outdated information, where changes over time lead to discrepancies; and finally, conflict due to misinformation, where factually incorrect information is presented.
To test how language models perform in these situations, the researchers created a dataset of 162 queries that included search results with conflicting knowledge. They then used this data to train and evaluate several different approaches to resolving conflicts, including a pipeline approach that uses multiple models to generate responses, an oracle approach that relies on human evaluation, and a taxonomy-aware approach that incorporates the new classification system.
The results showed that all three approaches struggled to some extent with resolving knowledge conflicts. However, the taxonomy-aware approach performed significantly better than the others, particularly when it came to identifying the type of conflict present in the search results.
This study highlights the importance of developing more advanced language models that can effectively handle conflicting knowledge. By incorporating a deeper understanding of these conflicts and how they arise, we may be able to create models that provide more accurate and reliable information to users. As our reliance on technology continues to grow, it’s essential that we prioritize the development of intelligent systems that can navigate complex information landscapes with ease.
The researchers’ findings also have implications for how we design search engines and other information retrieval systems.
Cite this article: “Resolving Conflicting Knowledge in Language Models: A Study on Handling Disagreement in Search Results”, The Science Archive, 2025.
Knowledge Conflicts, Language Models, Conflicting Information, Taxonomy Of Knowledge Conflicts, No Conflict, Complementary Information, Conflicting Opinions, Outdated Information, Misinformation, Search Engines, Information Retrieval Systems
