Table-based Fact Verification (TFV) aims to extract the entailment relation
between statements and structured tables. Existing TFV methods based on
small-scaled models suffer from insufficient labeled data and weak zero-shot
ability. Recently, the appearance of Large Language Models (LLMs) has gained
lots of attraction in research fields. They have shown powerful zero-shot and
in-context learning abilities on several NLP tasks, but their potential on TFV
is still unknown. In this work, we implement a preliminary study about whether
LLMs are table-based fact-checkers. In detail, we design diverse prompts to
explore how the in-context learning can help LLMs in TFV, i.e., zero-shot and
few-shot TFV capability. Besides, we carefully design and construct TFV
instructions to study the performance gain brought by the instruction tuning of
LLMs. Experimental results demonstrate that LLMs can achieve acceptable results
on zero-shot and few-shot TFV with prompt engineering, while instruction-tuning
can stimulate the TFV capability significantly. We also make some valuable
findings about the format of zero-shot prompts and the number of in-context
examples. Finally, we analyze some possible directions to promote the accuracy
of TFV via LLMs, which is beneficial to further research of table reasoning.

通过大型语言模型的提示工程和指令调整，可以在表格事实验证中实现接受的零样本和少样本验证能力，并且可以促进进一步通过大型语言模型提高表格推理的准确性的研究。

大型语言模型能否作为基于表格的事实核查工具？

Are Large Language Models Table-based Fact-Checkers?

The table-based fact verification task has recently gained widespread
attention and yet remains to be a very challenging problem. It inherently
requires informative reasoning over natural language together with different
numerical and logical reasoning on tables (e.g., count, superlative,
comparative). Considering that, we exploit mixture-of-experts and present in
this paper a new method: Self-adaptive Mixture-of-Experts Network (SaMoE).
Specifically, we have developed a mixture-of-experts neural network to
recognize and execute different types of reasoning -- the network is composed
of multiple experts, each handling a specific part of the semantics for
reasoning, whereas a management module is applied to decide the contribution of
each expert network to the verification result. A self-adaptive method is
developed to teach the management module combining results of different experts
more efficiently without external knowledge. The experimental results
illustrate that our framework achieves 85.1% accuracy on the benchmark dataset
TabFact, comparable with the previous state-of-the-art models. We hope our
framework can serve as a new baseline for table-based verification. Our code is
available at this https URL.

本文提出了一种基于自适应 Mixture-of-Experts 网络的方法用于表格事实验证，该方法有效结合了自然语言和不同类型的数理逻辑推理。实验结果表明，该方法可以取得与之前最先进的模型可比的 85.1% 的准确度。

基于表格的事实验证与自适应专家混合模型

Table-based Fact Verification with Self-adaptive Mixture of Experts

Table-based fact verification task aims to verify whether the given statement
is supported by the given semi-structured table. Symbolic reasoning with
logical operations plays a crucial role in this task. Existing methods leverage
programs that contain rich logical information to enhance the verification
process. However, due to the lack of fully supervised signals in the program
generation process, spurious programs can be derived and employed, which leads
to the inability of the model to catch helpful logical operations. To address
the aforementioned problems, in this work, we formulate the table-based fact
verification task as an evidence retrieval and reasoning framework, proposing
the Logic-level Evidence Retrieval and Graph-based Verification network
(LERGV). Specifically, we first retrieve logic-level program-like evidence from
the given table and statement as supplementary evidence for the table. After
that, we construct a logic-level graph to capture the logical relations between
entities and functions in the retrieved evidence, and design a graph-based
verification network to perform logic-level graph-based reasoning based on the
constructed graph to classify the final entailment relation. Experimental
results on the large-scale benchmark TABFACT show the effectiveness of the
proposed approach.

提出了 Logic-level Evidence Retrieval and Graph-based Verification network (LERGV) 框架，将基于表格的事实验证任务作为证据检索和推理框架来进行，通过检索逻辑级别证据和构建基于图的推理进行最终蕴含关系的分类。在大规模基准数据集 TABFACT 上的实验结果表明了所提出方法的有效性。