Question answering over hybrid contexts is a complex task, which requires the
combination of information extracted from unstructured texts and structured
tables in various ways. Recently, In-Context Learning demonstrated significant
performance advances for reasoning tasks. In this paradigm, a large language
model performs predictions based on a small set of supporting exemplars. The
performance of In-Context Learning depends heavily on the selection procedure
of the supporting exemplars, particularly in the case of HybridQA, where
considering the diversity of reasoning chains and the large size of the hybrid
contexts becomes crucial. In this work, we present Selection of ExEmplars for
hybrid Reasoning (SEER), a novel method for selecting a set of exemplars that
is both representative and diverse. The key novelty of SEER is that it
formulates exemplar selection as a Knapsack Integer Linear Program. The
Knapsack framework provides the flexibility to incorporate diversity
constraints that prioritize exemplars with desirable attributes, and capacity
constraints that ensure that the prompt size respects the provided capacity
budgets. The effectiveness of SEER is demonstrated on FinQA and TAT-QA, two
real-world benchmarks for HybridQA, where it outperforms previous exemplar
selection methods.

SEER 是一种选择代表性和多样性的示例集合的新方法，通过将示例选择问题转化为背包整数线性规划问题，通过灵活调整多样性和容量约束条件来提高在 HybridQA 中的性能。在 FinQA 和 TAT-QA 两个真实世界的 HybridQA 基准测试中，SEER 优于先前的示例选择方法。

SEER：一种用于上下文混合问答的背包法示例选择

SEER: A Knapsack approach to Exemplar Selection for In-Context HybridQA

Hybrid Question-Answering (HQA), which targets reasoning over tables and
passages linked from table cells, has witnessed significant research in recent
years. A common challenge in HQA and other passage-table QA datasets is that it
is generally unrealistic to iterate over all table rows, columns, and linked
passages to retrieve evidence. Such a challenge made it difficult for previous
studies to show their reasoning ability in retrieving answers. To bridge this
gap, we propose a novel Table-alignment-based Cell-selection and Reasoning
model (TACR) for hybrid text and table QA, evaluated on the HybridQA and
WikiTableQuestions datasets. In evidence retrieval, we design a
table-question-alignment enhanced cell-selection method to retrieve
fine-grained evidence. In answer reasoning, we incorporate a QA module that
treats the row containing selected cells as context. Experimental results over
the HybridQA and WikiTableQuestions (WTQ) datasets show that TACR achieves
state-of-the-art results on cell selection and outperforms fine-grained
evidence retrieval baselines on HybridQA, while achieving competitive
performance on WTQ. We also conducted a detailed analysis to demonstrate that
being able to align questions to tables in the cell-selection stage can result
in important gains from experiments of over 90\% table row and column selection
accuracy, meanwhile also improving output explainability.

我们提出了一种基于表格对齐的单元格选择和推理模型，它具有对齐问题到表格的能力，用于细粒度证据检索和答案推理，并在 HybridQA 数据集上实现了领先的结果。

TACR：一种基于表格对齐的混合问答中的单元格选择和推理模型

TACR: A Table-alignment-based Cell-selection and Reasoning Model for  Hybrid Question-Answering

Existing question answering datasets focus on dealing with homogeneous
information, based either only on text or KB/Table information alone. However,
as human knowledge is distributed over heterogeneous forms, using homogeneous
information alone might lead to severe coverage problems. To fill in the gap,
we present HybridQA this https URL, a new large-scale
question-answering dataset that requires reasoning on heterogeneous
information. Each question is aligned with a Wikipedia table and multiple
free-form corpora linked with the entities in the table. The questions are
designed to aggregate both tabular information and text information, i.e., lack
of either form would render the question unanswerable. We test with three
different models: 1) a table-only model. 2) text-only model. 3) a hybrid model
that combines heterogeneous information to find the answer. The experimental
results show that the EM scores obtained by two baselines are below 20\%, while
the hybrid model can achieve an EM over 40\%. This gap suggests the necessity
to aggregate heterogeneous information in HybridQA. However, the hybrid model's
score is still far behind human performance. Hence, HybridQA can serve as a
challenging benchmark to study question answering with heterogeneous
information.

HybridQA 是一个基于异构信息的新的大规模问答数据集，通过对表格和文本信息的聚合完成问答，结果表明使用异构信息的混合模型可以获得高于基准模型的精确度。