What Is Seen Cannot Be Unseen: The Disruptive Effect of Knowledge
  Conflict on Large Language Models
Large language models frequently rely on both contextual input and parametric
knowledge to perform tasks. However, these sources can come into conflict,
especially when retrieved documents contradict the model's parametric
knowledge. We propose a diagnostic framework to systematically evaluate LLM
behavior under context-memory conflict, where the contextual information
diverges from their parametric beliefs. We construct diagnostic data that
elicit these conflicts and analyze model performance across multiple task
types. Our findings reveal that (1) knowledge conflict has minimal impact on
tasks that do not require knowledge utilization, (2) model performance is
consistently higher when contextual and parametric knowledge are aligned, (3)
models are unable to fully suppress their internal knowledge even when
instructed, and (4) providing rationales that explain the conflict increases
reliance on contexts. These insights raise concerns about the validity of
model-based evaluation and underscore the need to account for knowledge
conflict in the deployment of LLMs.
68484fbb4fe3b60e21b2588f
Kaiser Sun
68484fbb4fe3b60e21b25890
Fan Bai
68484fbb4fe3b60e21b25891
Mark Dredze
What Is Seen Cannot Be Unseen: The Disruptive Effect of Knowledge
  Conflict on Large Language Models
Large language models frequently rely on both contextual input and parametric
knowledge to perform tasks. However, these sources can come into conflict,
especially when retrieved documents contradict the model's parametric
knowledge. We propose a diagnostic framework to systematically evaluate LLM
behavior under context-memory conflict, where the contextual information
diverges from their parametric beliefs. We construct diagnostic data that
elicit these conflicts and analyze model performance across multiple task
types. Our findings reveal that (1) knowledge conflict has minimal impact on
tasks that do not require knowledge utilization, (2) model performance is
consistently higher when contextual and parametric knowledge are aligned, (3)
models are unable to fully suppress their internal knowledge even when
instructed, and (4) providing rationales that explain the conflict increases
reliance on contexts. These insights raise concerns about the validity of
model-based evaluation and underscore the need to account for knowledge
conflict in the deployment of LLMs.
LLMs show varying performance under context-memory conflict, with conflicting knowledge impacting tasks that require knowledge utilization and model reliance on internal knowledge challenging to suppress.
Large language models contextual input parametric knowledge diagnostic framework context-memory conflict diagnostic data task types knowledge conflict