The Knowledge Alignment Problem: Bridging Human and External Knowledge for Large Language Models.

Shuo Zhang, Liangming Pan, Junzhou Zhao, William Yang Wang

Abstract:

Large language models often necessitate
grounding on external knowledge to generate
faithful and reliable answers. Yet even with
the correct groundings in the reference, they
can ignore them and rely on wrong groundings
or their inherent biases to hallucinate when
users, being largely unaware of the specifics
of the stored information, pose questions that
might not directly correlate with the retrieved
groundings. In this work, we formulate this
knowledge alignment problem and introduce
MixAlign, a framework that interacts with both
the human user and the knowledge base to obtain and integrate clarifications on how the user
question relates to the stored information. MixAlign employs a language model to achieve
automatic knowledge alignment and, if necessary, further enhances this alignment through
human user clarifications. Experimental results
highlight the crucial role of knowledge alignment in boosting model performance and mitigating hallucination, with improvements noted
up to 22.2% and 27.1% respectively. We also
demonstrate the effectiveness of MixAlign in
improving knowledge alignment by producing
high-quality, user-centered clarifications.