Understanding the Interplay between Parametric and Contextual Knowledge for Large Language Models

Sitao Cheng, Liangming Pan, Xunjian Yin, Xinyi Wang, William Yang Wang

Keywords: Large Language Models, Parametric Knowledge, Contextual Knowledge, Reasoning

TL;DR: We investigate the dynamic interaction between LLMs parametric knowledge and contextual knowledge.

Abstract: Large language models (LLMs) encode vast amounts of knowledge during pre-training (parametric knowledge or PK) and can further be enhanced by incorporating contextual knowledge (CK). Can LLMs effectively integrate their internal PK with external CK to solve complex problems? In this paper, we investigate the dynamic interaction between PK and CK, categorizing their relationships into Supportive, Complementary, Conflicting, and Irrelevant types. To support this investigation, we introduce EchoQA, a benchmark spanning scientific, factual, and commonsense knowledge. Our results show that LLMs tend to suppress their PK when contextual information is available, even when it is complementary or irrelevant. While tailored instructions can encourage LLMs to rely more on their PK, they still struggle to fully leverage it. These findings reveal a key vulnerability in LLMs, raising concerns about their reliability in knowledge-intensive tasks.

Primary Area: foundation or frontier models, including LLMs

Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.

Submission Guidelines: I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide.

Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.

No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.

Submission Number: 11963