Tug-of-War Between Knowledge: Exploring and Resolving Knowledge
  Conflicts in Retrieval-Augmented Language Models
Zhuoran Jin Pengfei Cao Yubo Chen Kang Liu Xiaojian Jiang Jiexin Xu Qiuxia Li Jun Zhao
Retrieval-augmented language models (RALMs) have demonstrated significant
potential in refining and expanding their internal memory by retrieving
evidence from external sources. However, RALMs will inevitably encounter
knowledge conflicts when integrating their internal memory with external
sources. Knowledge conflicts can ensnare RALMs in a tug-of-war between
knowledge, limiting their practical applicability. In this paper, we focus on
exploring and resolving knowledge conflicts in RALMs. First, we present an
evaluation framework for assessing knowledge conflicts across various
dimensions. Then, we investigate the behavior and preference of RALMs from the
following two perspectives: (1) Conflicts between internal memory and external
sources: We find that stronger RALMs emerge with the Dunning-Kruger effect,
persistently favoring their faulty internal memory even when correct evidence
is provided. Besides, RALMs exhibit an availability bias towards common
knowledge; (2) Conflicts between truthful, irrelevant and misleading evidence:
We reveal that RALMs follow the principle of majority rule, leaning towards
placing trust in evidence that appears more frequently. Moreover, we find that
RALMs exhibit confirmation bias, and are more willing to choose evidence that
is consistent with their internal memory. To solve the challenge of knowledge
conflicts, we propose a method called Conflict-Disentangle Contrastive Decoding
(CD2) to better calibrate the model's confidence. Experimental results
demonstrate that our CD2 can effectively resolve knowledge conflicts in RALMs.