  Despite advancements in LLMs, knowledge-based reasoning remains a
longstanding issue due to the fragility of knowledge recall and inference.
Existing methods primarily encourage LLMs to autonomously plan and solve
problems or to extensively sample reasoning chains without addressing the
conceptual and inferential fallacies. Attempting to alleviate inferential
fallacies and drawing inspiration from multi-agent collaboration, we present a
framework to increase faithfulness and causality for knowledge-based reasoning.
Specifically, we propose to employ multiple intelligent agents (i.e., reasoners
and an evaluator) to work collaboratively in a reasoning-and-consensus paradigm
for elevated reasoning faithfulness. The reasoners focus on providing solutions
with human-like causality to solve open-domain problems. On the other hand, the
\textit{evaluator} agent scrutinizes if a solution is deducible from a
non-causal perspective and if it still holds when challenged by a
counterfactual candidate. According to the extensive and comprehensive
evaluations on a variety of knowledge reasoning tasks (e.g., science question
answering and commonsense reasoning), our framework outperforms all compared
state-of-the-art approaches by large margins.

Artificial Intelligence;Multiagent Systems