Resolving Knowledge Conflicts in Large Language Models
Yike Wang Shangbin Feng Heng Wang Weijia Shi Vidhisha Balachandran Tianxing He Yulia Tsvetkov
Large language models (LLMs) often encounter knowledge conflicts, scenarios
where discrepancy arises between the internal parametric knowledge of LLMs and
non-parametric information provided in the prompt context. In this work we ask
what are the desiderata for LLMs when a knowledge conflict arises and whether
existing LLMs fulfill them. We posit that LLMs should 1) identify knowledge
conflicts, 2) pinpoint conflicting information segments, and 3) provide
distinct answers or viewpoints in conflicting scenarios. To this end, we
introduce KNOWLEDGE CONFLICT, an evaluation framework for simulating contextual
knowledge conflicts and quantitatively evaluating to what extent LLMs achieve
these goals. KNOWLEDGE CONFLICT includes diverse and complex situations of
knowledge conflict, knowledge from diverse entities and domains, two synthetic
conflict creation methods, and settings with progressively increasing
difficulty to reflect realistic knowledge conflicts. Extensive experiments with
the KNOWLEDGE CONFLICT framework reveal that while LLMs perform well in
identifying the existence of knowledge conflicts, they struggle to determine
the specific conflicting knowledge and produce a response with distinct answers
amidst conflicting information. To address these challenges, we propose new
instruction-based approaches that augment LLMs to better achieve the three
goals. Further analysis shows that abilities to tackle knowledge conflicts are
greatly impacted by factors such as knowledge domain and prompt text, while
generating robust responses to knowledge conflict scenarios remains an open
research question.