Long-form numerical reasoning in financial analysis aims to generate a reasoning program to calculate the correct answer for a given question. Previous work followed a retriever-generator framework, where the retriever selects key facts from a long-form document, and the generator generates a reasoning program based on retrieved facts. However, they treated all facts equally without considering the different contributions of facts with and without numbers. Meanwhile, the program consistency were ignored under supervised training, resulting in lower training accuracy and diversity. To solve these problems, we proposed APOLLO to improve the long-form numerical reasoning framework. For the retriever, we adopt a number-aware negative sampling strategy to enable the retriever to be more discriminative on key numerical facts. For the generator, we design consistency-based reinforcement learning and target program augmentation strategy based on the consistency of program execution results. Experimental results on the FinQA and ConvFinQA leaderboard verify the effectiveness of our proposed method, achieving the new state-of-the-art.

金融领域的数值推理 - 进行定量分析并总结了财务报告中的信息 - 可以大大提高业务效率并降低数十亿美元的成本。在这里，我们提出了一个数值推理问答系统，以回答财务文本和表数据源之间的数值推理问题，该问题由回收器模块，发电机模块和集合模块组成。具体而言，除了检索整个行数据外，我们还创新设计了一个细胞回收器，该池检索器可以检索金单元，以避免将同一行中的无关和相似的单元带到发电机模块的输入中。在发电机模块中，我们利用多个发电机来生产程序，这是回答问题的操作步骤。最后，在整体模块中，我们集成了多个程序，以选择最佳程序作为系统的输出。在FinQA竞争中的最终私人测试集中，我们的系统获得了69.79的执行精度。

Hybrid tabular-textual question answering (QA) requires reasoning from heterogeneous information, and the types of reasoning are mainly divided into numerical reasoning and span extraction. Despite being the main challenge of the task compared to extractive QA, current numerical reasoning method simply uses LSTM to autoregressively decode program sequences, and each decoding step produces either an operator or an operand. However, the step-by-step decoding suffers from exposure bias, and the accuracy of program generation drops sharply with progressive decoding. In this paper, we propose a non-autoregressive program generation framework, which facilitates program generation in parallel. Our framework, which independently generates complete program tuples containing both operators and operands, can significantly boost the speed of program generation while addressing the error accumulation issue. Our experiments on the MultiHiertt dataset shows that our model can bring about large improvements (+7.97 EM and +6.38 F1 points) over the strong baseline, establishing the new state-of-the-art performance, while being much faster (21x) in program generation. The performance drop of our method is also significantly smaller than the baseline with increasing numbers of numerical reasoning steps.

作为自然语言处理领域的后起之秀，在各行各业中，问答系统（问答系统）被广泛使用。与其他方案相比，在Q＆A系统的可追溯性和解释性方面，财务方案的应用程序有很强的要求。此外，由于对人工智能技术的需求已从最初的计算智能转变为认知智能，因此这项研究主要集中于财务数值推理数据集-FinQA。在共享任务中，目标是根据包含文本和表的给定财务报告生成推理程序和最终答案。我们使用基于Deberta预训练的语言模型的方法，并采用其他优化方法，包括在此基础上进行多模型融合，训练集组合。我们最终获得了68.99的执行精度和64.53的程序精度，在2022 FinQA挑战中排名第4。

Structured tabular data exist across nearly all fields. Reasoning task over these data aims to answer questions or determine the truthiness of hypothesis sentences by understanding the semantic meaning of a table. While previous works have devoted significant efforts to the tabular reasoning task, they always assume there are sufficient labeled data. However, constructing reasoning samples over tables (and related text) is labor-intensive, especially when the reasoning process is complex. When labeled data is insufficient, the performance of models will suffer an unendurable decline. In this paper, we propose a unified framework for unsupervised complex tabular reasoning (UCTR), which generates sufficient and diverse synthetic data with complex logic for tabular reasoning tasks, assuming no human-annotated data at all. We first utilize a random sampling strategy to collect diverse programs of different types and execute them on tables based on a "Program-Executor" module. To bridge the gap between the programs and natural language sentences, we design a powerful "NL-Generator" module to generate natural language sentences with complex logic from these programs. Since a table often occurs with its surrounding texts, we further propose novel "Table-to-Text" and "Text-to-Table" operators to handle joint table-text reasoning scenarios. This way, we can adequately exploit the unlabeled table resources to obtain a well-performed reasoning model under an unsupervised setting. Our experiments cover different tasks (question answering and fact verification) and different domains (general and specific), showing that our unsupervised methods can achieve at most 93% performance compared to supervised models. We also find that it can substantially boost the supervised performance in low-resourced domains as a data augmentation technique. Our code is available at https://github.com/leezythu/UCTR.

Recently, there has been significant progress in teaching language models to perform step-by-step reasoning to solve complex numerical reasoning tasks. Chain-of-thoughts prompting (CoT) is by far the state-of-art method for these tasks. CoT uses language models to perform both reasoning and computation in the multi-step `thought' process. To disentangle computation from reasoning, we propose `Program of Thoughts' (PoT), which uses language models (mainly Codex) to express the reasoning process as a program. The computation is relegated to an external computer, which executes the generated programs to derive the answer. We evaluate PoT on five math word problem datasets (GSM, AQuA, SVAMP, TabMWP, MultiArith) and three financial-QA datasets (FinQA, ConvFinQA, TATQA) for both few-shot and zero-shot setups. Under both few-shot and zero-shot settings, PoT can show an average performance gain over CoT by around 12\% across all the evaluated datasets. By combining PoT with self-consistency decoding, we can achieve SoTA performance on all math problem datasets and near-SoTA performance on financial datasets. All of our data and code are released in Github\footnote{\url{https://github.com/wenhuchen/Program-of-Thoughts}}.

大型语言模型在各种任务上显示出令人印象深刻的几次结果。但是，当知识是此类结果的关键时，就像问题回答和事实检查之类的任务一样，似乎需要存储知识的大量参数计数。众所周知，检索增强模型可以在不需要多个参数的情况下在知识密集的任务上表现出色，但是目前尚不清楚它们是否在几个弹药设置中工作。在这项工作中，我们介绍了地图集，这是一个经过精心设计和预先训练的增强语言模型，能够通过很少的培训示例学习知识密集型任务。我们对包括MMLU，苏格兰短裙和归类等各种任务进行评估，并研究文档索引内容的影响，表明它可以很容易地进行更新。值得注意的是，在自然问题上仅使用64个示例在自然问题上达到超过42 \％的准确性，尽管参数少了50倍，但比540B参数模型的表现优于540b参数模型。

Large pre-trained language models have been shown to store factual knowledge in their parameters, and achieve state-of-the-art results when fine-tuned on downstream NLP tasks. However, their ability to access and precisely manipulate knowledge is still limited, and hence on knowledge-intensive tasks, their performance lags behind task-specific architectures. Additionally, providing provenance for their decisions and updating their world knowledge remain open research problems. Pre-trained models with a differentiable access mechanism to explicit nonparametric memory can overcome this issue, but have so far been only investigated for extractive downstream tasks. We explore a general-purpose fine-tuning recipe for retrieval-augmented generation (RAG) -models which combine pre-trained parametric and non-parametric memory for language generation. We introduce RAG models where the parametric memory is a pre-trained seq2seq model and the non-parametric memory is a dense vector index of Wikipedia, accessed with a pre-trained neural retriever. We compare two RAG formulations, one which conditions on the same retrieved passages across the whole generated sequence, and another which can use different passages per token. We fine-tune and evaluate our models on a wide range of knowledge-intensive NLP tasks and set the state of the art on three open domain QA tasks, outperforming parametric seq2seq models and task-specific retrieve-and-extract architectures. For language generation tasks, we find that RAG models generate more specific, diverse and factual language than a state-of-the-art parametric-only seq2seq baseline.

在现实世界中的问题回答场景中，将表格和文本内容均结合的混合形式吸引了越来越多的关注，其中数值推理问题是最典型和最具挑战性的问题之一。现有方法通常采用编码器框架来表示混合内容并生成答案。但是，它无法捕获编码器侧数值，表格架构和文本信息之间的丰富关系。解码器使用一个简单的预定义运算符分类器，该分类器的灵活性不足以处理具有不同表达式的数值推理过程。为了解决这些问题，本文提出了一个\ textbf {re} lational \ textbf {g} raph增强\ textbf {h} ybrid table-text \ textbf {n}带有\ textbf {t textbf {t text} ree decoder（\ textbff recoder（\ textbf） {reghnt}）。它模拟了对表 - 文本混合内容的回答的数值问题，作为表达树的生成任务。此外，我们提出了一种新颖的关系图建模方法，该方法模拟了问题，表和段落之间的对齐方式。我们验证了公开可用的Table-Text混合质量质量质量标准（TAT-QA）的模型。拟议的reghnt显着胜过基线模型，并实现最新结果\脚注{我们在〜\ url {https://github.com/lfy79001/reghnt}}}〜（20222）公开发布了源代码和数据-05-05）。

Natural Language Generation (NLG) has improved exponentially in recent years thanks to the development of sequence-to-sequence deep learning technologies such as Transformer-based language models. This advancement has led to more fluent and coherent NLG, leading to improved development in downstream tasks such as abstractive summarization, dialogue generation and data-to-text generation. However, it is also apparent that deep learning based generation is prone to hallucinate unintended text, which degrades the system performance and fails to meet user expectations in many real-world scenarios. To address this issue, many studies have been presented in measuring and mitigating hallucinated texts, but these have never been reviewed in a comprehensive manner before. In this survey, we thus provide a broad overview of the research progress and challenges in the hallucination problem in NLG. The survey is organized into two parts: (1) a general overview of metrics, mitigation methods, and future directions; and (2) an overview of task-specific research progress on hallucinations in the following downstream tasks, namely abstractive summarization, dialogue generation, generative question answering, data-to-text generation, machine translation, and visual-language generation. This survey serves to facilitate collaborative efforts among researchers in tackling the challenge of hallucinated texts in NLG.

Mathematical reasoning is a fundamental aspect of human intelligence and is applicable in various fields, including science, engineering, finance, and everyday life. The development of artificial intelligence (AI) systems capable of solving math problems and proving theorems has garnered significant interest in the fields of machine learning and natural language processing. For example, mathematics serves as a testbed for aspects of reasoning that are challenging for powerful deep learning models, driving new algorithmic and modeling advances. On the other hand, recent advances in large-scale neural language models have opened up new benchmarks and opportunities to use deep learning for mathematical reasoning. In this survey paper, we review the key tasks, datasets, and methods at the intersection of mathematical reasoning and deep learning over the past decade. We also evaluate existing benchmarks and methods, and discuss future research directions in this domain.

Fact verification has attracted a lot of research attention recently, e.g., in journalism, marketing, and policymaking, as misinformation and disinformation online can sway one's opinion and affect one's actions. While fact-checking is a hard task in general, in many cases, false statements can be easily debunked based on analytics over tables with reliable information. Hence, table-based fact verification has recently emerged as an important and growing research area. Yet, progress has been limited due to the lack of datasets that can be used to pre-train language models (LMs) to be aware of common table operations, such as aggregating a column or comparing tuples. To bridge this gap, in this paper we introduce PASTA, a novel state-of-the-art framework for table-based fact verification via pre-training with synthesized sentence-table cloze questions. In particular, we design six types of common sentence-table cloze tasks, including Filter, Aggregation, Superlative, Comparative, Ordinal, and Unique, based on which we synthesize a large corpus consisting of 1.2 million sentence-table pairs from WikiTables. PASTA uses a recent pre-trained LM, DeBERTaV3, and further pretrains it on our corpus. Our experimental results show that PASTA achieves new state-of-the-art performance on two table-based fact verification benchmarks: TabFact and SEM-TAB-FACTS. In particular, on the complex set of TabFact, which contains multiple operations, PASTA largely outperforms the previous state of the art by 4.7 points (85.6% vs. 80.9%), and the gap between PASTA and human performance on the small TabFact test set is narrowed to just 1.5 points (90.6% vs. 92.1%).

当前的密集文本检索模型面临两个典型的挑战。首先，他们采用暹罗双重编码架构来独立编码查询和文档，以快速索引和搜索，同时忽略了较细粒度的术语互动。这导致了次优的召回表现。其次，他们的模型培训高度依赖于负面抽样技术，以在其对比损失中构建负面文档。为了应对这些挑战，我们提出了对抗猎犬速率（AR2），它由双重编码猎犬加上跨编码器等级组成。这两种模型是根据最小群体对手的共同优化的：检索员学会了检索负面文件以欺骗排名者，而排名者学会了对包括地面和检索的候选人进行排名，并提供渐进的直接反馈对双编码器检索器。通过这款对抗性游戏，猎犬逐渐生产出更难的负面文件来训练更好的排名者，而跨编码器排名者提供了渐进式反馈以改善检索器。我们在三个基准测试基准上评估AR2。实验结果表明，AR2始终如一地胜过现有的致密回收者方法，并在所有这些方法上实现了新的最新结果。这包括对自然问题的改进R@5％至77.9％（+2.1％），Triviaqa R@5％至78.2％（+1.4）和MS-Marco MRR@10％至39.5％（+1.3％）。代码和型号可在https://github.com/microsoft/ar2上找到。

数学推理是人类智力的核心能力，在抽象思维和逻辑推理中对机器提出了独特的挑战。最近的大型预训练的语言模型（例如GPT-3）在以文本形式（例如数学单词问题（MWP））编写的数学推理任务上取得了显着的进步。但是，未知模型是否可以处理更复杂的问题，这些问题涉及数学推理，例如表格数据。为了填补空白，我们提出了表格数学单词问题（TABMWP），这是一个包含38,431个开放域级等级问题的新数据集，这些问题需要在文本和表格数据上进行数学推理。 TABMWP中的每个问题都与表格上下文对齐，该上下文作为图像，半结构化文本和结构化表。有两种类型的问题：自由文本和多选择，每个问题都用金解决方案注释以揭示多步推理过程。我们在TABMWP上评估了不同的预训练模型，包括在几次设置中的GPT-3模型。正如先前的研究所表明的那样，由于很少有GPT-3依赖于内在的示例的选择，因此其性能是不稳定的，并且可能会降解为几乎机会。处理TABMWP等复杂问题时，不稳定的问题更为严重。为了减轻这种情况，我们进一步提出了一种新颖的方法，即PresspG，该方法利用策略梯度学习从少量培训数据中选择中文示例，然后为测试示例构造相应的提示。实验结果表明，与随机选择相比，我们的方法在准确性度量上优于最佳基线，并显着降低了预测方差，这验证了其在选择性上下文示例中的有效性。

知识密集型任务，例如开放域问题答案（QA），需要访问大量的世界知识或领域知识。知识密集型任务的一种常见方法是采用检索到阅读的管道，该管道首先从诸如Wikipedia之类的外部语料库中检索少数相关的上下文文档，然后预测在检索文档的条件下得到答案。在本文中，我们提出了一种新的观点，可以通过用大型语言模型生成器代替文档检索器来解决知识密集型任务。我们称我们的方法生成-Read Read（GenRead），该方法首先提示大型语言模型根据给定问题生成上下文文档，然后读取生成的文档以产生最终答案。此外，我们提出了一种基于聚类的提示方法，该方法选择了不同的提示，从而产生了涵盖不同观点的生成文档，从而更好地回忆了可接受的答案。我们对三个不同的知识密集任务进行了广泛的实验，包括开放域质量检查，事实检查和对话系统。值得注意的是，GenRead在Triviaqa和WebQ上实现了71.6和54.4的精确匹配分数，显着超过了最先进的检索到+4.0和+3.9的最先进的dpr-fid，而无需从任何外部知识源中检索任何文档。最后，我们证明可以通过结合检索和生成来进一步提高模型性能。

Table-and-text hybrid question answering (HybridQA) is a widely used and challenging NLP task commonly applied in the financial and scientific domain. The early research focuses on migrating other QA task methods to HybridQA, while with further research, more and more HybridQA-specific methods have been present. With the rapid development of HybridQA, the systematic survey is still under-explored to summarize the main techniques and advance further research. So we present this work to summarize the current HybridQA benchmarks and methods, then analyze the challenges and future directions of this task. The contributions of this paper can be summarized in three folds: (1) first survey, to our best knowledge, including benchmarks, methods and challenges for HybridQA; (2) systematic investigation with the reasonable comparison of the existing systems to articulate their advantages and shortcomings; (3) detailed analysis of challenges in four important dimensions to shed light on future directions.

最近的大规模预训练的进步，例如GPT-3允许从给定提示生成看似高质量的文本。然而，这种一代系统经常遭受幻觉的事实问题，并且本身并不是旨在包含有用的外部信息。接地的代表似乎提供了补救措施，但他们的培训通常依赖于提供信息相关文件的很少可用的并行数据。我们提出了一个框架，通过在语言模型信号上共同训练接地的发生器和文档检索来缓解这种数据约束。该模型学会奖励具有生成中最高效用的文档的检索，并用专家混合（MOE）合并来术语术，以产生后续文本。我们证明，发电机和猎犬都可以利用这种联合培训，协同作用，以生产散文和对话一代中的更多信息和相关文本。

我们提出了一种用于在生成答案时将信息与多个检索文件中的信息组合的可检索增强的开放式开放式开放式开放域问题训练方法。我们将检索决策模拟作为相关文件集的潜在变量。由于通过对所检索的文件集的边缘化，因此使用期望最大化算法估计这一点。我们迭代地估计我们的潜在变量的价值（给定问题的这些相关文档集），然后使用此估计来更新检索器和读取器参数。我们假设这种端到端的训练允许训练信号流到读者，然后比上演明智的训练更好地流到猎犬。这导致检索器能够为问题和读者选择更多相关文档，这些文件在更准确的文档中培训以生成答案。三个基准数据集的实验表明，我们所提出的方法优于所有现有的相当大小的方法2-3％绝对精确匹配点，实现了新的最先进的结果。我们的结果还展示了学习检索以改善答复的可行性，而无明确监督检索决策。

Reasoning, as an essential ability for complex problem-solving, can provide back-end support for various real-world applications, such as medical diagnosis, negotiation, etc. This paper provides a comprehensive survey of cutting-edge research on reasoning with language model prompting. We introduce research works with comparisons and summaries and provide systematic resources to help beginners. We also discuss the potential reasons for emerging such reasoning abilities and highlight future research directions.

Despite the success of large language models (LLMs) in various natural language processing (NLP) tasks, the stored knowledge in these models may inevitably be incomplete, out-of-date, or incorrect. This motivates the need to utilize external knowledge to assist LLMs. Unfortunately, current methods for incorporating external knowledge often require additional training or fine-tuning, which can be costly and may not be feasible for LLMs. To address this issue, we propose a novel post-processing approach, rethinking with retrieval (RR), which retrieves relevant external knowledge based on the decomposed reasoning steps obtained from the chain-of-thought (CoT) prompting. This lightweight approach does not require additional training or fine-tuning and is not limited by the input length of LLMs. We evaluate the effectiveness of RR through extensive experiments with GPT-3 on three complex reasoning tasks: commonsense reasoning, temporal reasoning, and tabular reasoning. Our results show that RR can produce more faithful explanations and improve the performance of LLMs.