Large Language Models (LLMs) have shown to be capable of various tasks, yet
their capability in interpreting and reasoning over tabular data remains an
underexplored area. In this context, this study investigates from three core
perspectives: the robustness of LLMs to structural perturbations in tables, the
comparative analysis of textual and symbolic reasoning on tables, and the
potential of boosting model performance through the aggregation of multiple
reasoning pathways. We discover that structural variance of tables presenting
the same content reveals a notable performance decline, particularly in
symbolic reasoning tasks. This prompts the proposal of a method for table
structure normalization. Moreover, textual reasoning slightly edges out
symbolic reasoning, and a detailed error analysis reveals that each exhibits
different strengths depending on the specific tasks. Notably, the aggregation
of textual and symbolic reasoning pathways, bolstered by a mix self-consistency
mechanism, resulted in achieving SOTA performance, with an accuracy of 73.6% on
WIKITABLEQUESTIONS, representing a substantial advancement over previous
existing table processing paradigms of LLMs.

通过对大型语言模型的研究，揭示了它们在解释和推理表格数据方面的能力，发现表格结构变化对模型性能的影响，提出了表格结构归一化的方法，并且比较了文本推理和符号推理，同时通过多个推理路径的聚合，取得了在 WIKITABLEQUESTIONS 任务上的最新成果。

用大型语言模型重新思考表格数据理解

Rethinking Tabular Data Understanding with Large Language Models

Existing Scene Text Recognition (STR) methods typically use a language model
to optimize the joint probability of the 1D character sequence predicted by a
visual recognition (VR) model, which ignore the 2D spatial context of visual
semantics within and between character instances, making them not generalize
well to arbitrary shape scene text. To address this issue, we make the first
attempt to perform textual reasoning based on visual semantics in this paper.
Technically, given the character segmentation maps predicted by a VR model, we
construct a subgraph for each instance, where nodes represent the pixels in it
and edges are added between nodes based on their spatial similarity. Then,
these subgraphs are sequentially connected by their root nodes and merged into
a complete graph. Based on this graph, we devise a graph convolutional network
for textual reasoning (GTR) by supervising it with a cross-entropy loss. GTR
can be easily plugged in representative STR models to improve their performance
owing to better textual reasoning. Specifically, we construct our model, namely
S-GTR, by paralleling GTR to the language model in a segmentation-based STR
baseline, which can effectively exploit the visual-linguistic complementarity
via mutual learning. S-GTR sets new state-of-the-art on six challenging STR
benchmarks and generalizes well to multi-linguistic datasets. Code is available
at this https URL

该研究提出了一种基于图卷积网络的文本推理（GTR）方法并将其应用于场景文本识别中，该方法可以利用像素之间的空间关联来提高文本识别的性能，并在六个具有挑战性的基准测试中获得最新的最佳结果。

视觉语义提高了场景文本识别中的文本推理水平

Visual Semantics Allow for Textual Reasoning Better in Scene Text  Recognition

This paper is concerned with the task of multi-hop open-domain Question
Answering (QA). This task is particularly challenging since it requires the
simultaneous performance of textual reasoning and efficient searching. We
present a method for retrieving multiple supporting paragraphs, nested amidst a
large knowledge base, which contain the necessary evidence to answer a given
question. Our method iteratively retrieves supporting paragraphs by forming a
joint vector representation of both a question and a paragraph. The retrieval
is performed by considering contextualized sentence-level representations of
the paragraphs in the knowledge source. Our method achieves state-of-the-art
performance over two well-known datasets, SQuAD-Open and HotpotQA, which serve
as our single- and multi-hop open-domain QA benchmarks, respectively.

本文提出了一种方法，用于在大量知识库之中检索多个支持段落，以回答所提出的问题。该方法使用联合向量表示形式，并考虑知识源中段落的上下文句子表示形式。该技术在 SQuAD-Open 和 HotpotQA 的数据集上实现了开放域问答的最新成果。