How Knowledge Popularity Influences and Enhances LLMs’ Perception of Knowledge Boundaries

ACL ARR 2025 May Submission1456 Authors

Abstract:

Large language models (LLMs) often fail to recognize their knowledge boundaries, producing confident yet incorrect answers.
In this paper, we investigate how knowledge popularity affects LLMs’ ability to perceive their knowledge boundaries.
Focusing on entity-centric factual question answering (QA), we quantify knowledge popularity from three perspectives: the popularity of entities in the question, the popularity of entities in the answer, and relation popularity, defined as their co-occurrence frequency.
Experiments on three representative datasets containing knowledge with varying popularity show that LLMs exhibit better QA performance, higher confidence, and more accurate perception on more popular knowledge, with relation popularity having the strongest correlation.
Cause knowledge popularity shows strong correlation with LLMs' QA performance, we propose to leverage these signals for confidence calibration. This improves the accuracy of answer correctness prediction by an average of 5.24\% across all models and datasets.
Furthermore, we explore prompting LLMs to estimate popularity without external corpora, which yields a viable alternative.

Paper Type: Long

Research Area: Question Answering

Research Area Keywords: LLM Knowledge Boundary Perception, Question-Answering

Contribution Types: Data analysis

Languages Studied: English

Submission Number: 1456