In this work, we address question answering (QA) over a hybrid of tabular and
textual data that are very common content on the Web (e.g. SEC filings), where
discrete reasoning capabilities are often required. Recently, large language
models (LLMs) like GPT-4 have demonstrated strong multi-step reasoning
capabilities. We then consider harnessing the amazing power of LLMs to solve
our task. We abstract a Step-wise Pipeline for tabular and textual QA, which
consists of three key steps, including Extractor, Reasoner and Executor, and
initially design an instruction to instantiate the pipeline and validate that
GPT-4 outperforms all existing methods. However, utilizing an online LLM like
GPT-4 holds various challenges in terms of cost, latency, and data security
risk, which motivates us to specialize smaller LLMs in this task. We develop a
TAT-LLM language model by fine-tuning LLaMA 2 with the training data generated
automatically from existing expert-annotated datasets following the Step-wise
Pipeline. The experimental results have verified that our TAT-LLM model can
outperform all baseline models, including the previous best fine-tuned models
and very large-scale LLMs like GPT-4 on FinQA, TAT-QA and TAT-DQA benchmarks.
We hope our work can serve as a pioneering example of specializing smaller
language models for specific tasks.

我们开发了一个 TAT-LLM 语言模型来针对特定任务，该模型在 FinQA、TAT-QA 和 TAT-DQA 基准测试中表现优于所有基准模型，包括先前的最佳微调模型和像 GPT-4 这样的大规模语言模型。