Temporal Knowledge Question Answering via Abstract Reasoning Induction
Ziyang Chen Dongfang Li Xiang Zhao Baotian Hu Min Zhang
In this paper, we tackle the significant challenge of temporal knowledge
reasoning in Large Language Models (LLMs), an area where such models frequently
encounter difficulties. These difficulties often result in the generation of
misleading or incorrect information, primarily due to their limited capacity to
process evolving factual knowledge and complex temporal logic. In response, we
propose a novel, constructivism-based approach that advocates for a paradigm
shift in LLM learning towards an active, ongoing process of knowledge synthesis
and customization. At the heart of our proposal is the Abstract Reasoning
Induction ARI framework, which divides temporal reasoning into two distinct
phases: Knowledge-agnostic and Knowledge-based. This division aims to reduce
instances of hallucinations and improve LLMs' capacity for integrating abstract
methodologies derived from historical data. Our approach achieves remarkable
improvements, with relative gains of 29.7\% and 9.27\% on two temporal QA
datasets, underscoring its efficacy in advancing temporal reasoning in LLMs.
The code will be released at https://github.com/czy1999/ARI.