## 1 Introduction
Due to the lack of space, the original claims are only shown in the figure, and the translations (symbol #7) of claims and parts of evidence are offered as the central part to better illustrate the evidence. checking tool could verify new claims, extract related evidence, and track the evolution. [PERSON], [PERSON] et al. [8] leveraged an evidence-conditioned perplexity score from the masked language model for claim verification, which is an unsupervised method. In the fine-gained scenario, [PERSON], [PERSON] and [PERSON] [9] determined the veracity of a claim by adopting an attention-based evidence aggregator that used a multilingual transformer-based model, and concatenated the additional metadata of a claim to augment the model. The unified models based on pre-training and fine-tuning are the mainstream fact-checking paradigm, for example, designing the evidence-conditioned perplexity function or evidence aggregator based on the attention mechanism for fact verification, which is mainly trained for learning representation patterns and joint probability distributions of factual claims and pieces of evidence. The methods indicated a feasible inference idea; the core of this is that with deep semantic understanding from multilingual pre-training models, a downstream classifier can be progressively fine-tuned based on a training dataset in a certain scale. In consequence, sufficient evidence of aggregation and metadata supplementation for fact verification contributes to strengthening the authenticity inference calculation. However, in the scenario of few-shot or zero-shot samples across domains and languages, the learning perception ability of the previous models is extremely limited. The main reasons are as follows: On the one hand, due to the lack of large-scale data driving, the model cannot be quickly and finely fine-tuned to the downstream tasks, and the potential of pre-training models is desired to be further stimulated. On the other hand, the existing coarse-grained evidence aggregation mechanism, which requires more fine-grained evidence chains, cannot provide acceptable evidence. Last, but not the least, the previous models have not considered leveraging the consistency of the same language family in the fact statement representation paradigm to deal with the correlation learning mechanism of the lifting model, thus, strengthening the transfer learning ability of the model in few-/zero-shot scenarios. To deal with the above issues, we propose the Joint Prompt and Evidence Inference Network (henceforth, PEINet) framework for multilingual fact-checking in the zero-shot scene, which is inspired by the human cognitive paradigm. When the human brain judges whether a fact is true or false, the first decision-making consciousness is to take aim at the claim or title. If there is an excessively exaggerated or obviously outrageous expression in the statement, we can directly determine that it is false. Of course, for an obscure and complicated statement, next it is necessary to retrieve relevant evidence and extract reasonable clues, so as to generate final judgment. Furthermore, in the face of fact-checking in terra incognita, relevance cognition plays an important role in the brain learning mechanism. In general, claims in a similar-expression paradigm may have consistent authenticity, and similar fact statements described in the same language family may share the like expression paradigms. In order to better clarify the mechanism, we take a concrete example, as shown in Figure 1. Through comprehensive comparison and observation of examples #1 and #2, we discover that the claim in #1 obviously contains exaggerated and deliberate contents that are highlighted with roughening and underlining in the figure, which evidently is false from logical empiricism. Furthermore, the claim in #2 is relatively objective.