Pseudocode in a scholarly paper provides a concise way to express the
algorithms implemented therein. Pseudocode can also be thought of as an
intermediary representation that helps bridge the gap between programming
languages and natural languages. Having access to a large collection of
pseudocode can provide various benefits ranging from enhancing algorithmic
understanding, facilitating further algorithmic design, to empowering NLP or
computer vision based models for tasks such as automated code generation and
optical character recognition (OCR). We have created a large pseudocode
collection by extracting nearly 320,000 pseudocode examples from arXiv papers.
This process involved scanning over $2.2$ million scholarly papers, with 1,000
of them being manually inspected and labeled. Our approach encompasses an
extraction mechanism tailored to optimize the coverage and a validation
mechanism based on random sampling to check its accuracy and reliability, given
the inherent heterogeneity of the collection. In addition, we offer insights
into common pseudocode structures, supported by clustering and statistical
analyses. Notably, these analyses indicate an exponential-like growth in the
usage of pseudocodes, highlighting their increasing significance.

通过从 arXiv 论文中提取近 320,000 个伪代码示例，我们创建了一个庞大的伪代码集合。我们的方法包括一个定制化的提取机制和一个基于随机抽样的验证机制，以优化覆盖范围并检查准确性和可靠性。同时，通过聚类和统计分析，我们提供了对常见伪代码结构的洞察，表明伪代码的使用呈现出指数型增长，凸显其不断增加的重要性。

自动提取伪代码的扩展规模

Scaling Automatic Extraction of Pseudocode

Algorithmic reasoning refers to the ability to understand the complex
patterns behind the problem and decompose them into a sequence of reasoning
steps towards the solution. Such nature of algorithmic reasoning makes it a
challenge for large language models (LLMs), even though they have demonstrated
promising performance in other reasoning tasks. Within this context, some
recent studies use programming languages (e.g., Python) to express the
necessary logic for solving a given instance/question (e.g.,
Program-of-Thought) as inspired by their strict and precise syntaxes. However,
it is non-trivial to write an executable code that expresses the correct logic
on the fly within a single inference call. Also, the code generated
specifically for an instance cannot be reused for others, even if they are from
the same task and might require identical logic to solve. This paper presents
Think-and-Execute, a novel framework that decomposes the reasoning process of
language models into two steps. (1) In Think, we discover a task-level logic
that is shared across all instances for solving a given task and then express
the logic with pseudocode; (2) In Execute, we further tailor the generated
pseudocode to each instance and simulate the execution of the code. With
extensive experiments on seven algorithmic reasoning tasks, we demonstrate the
effectiveness of Think-and-Execute. Our approach better improves LMs' reasoning
compared to several strong baselines performing instance-specific reasoning
(e.g., CoT and PoT), suggesting the helpfulness of discovering task-level
logic. Also, we show that compared to natural language, pseudocode can better
guide the reasoning of LMs, even though they are trained to follow natural
language instructions.

该论文介绍了一种名为 Think-and-Execute 的新框架，它将语言模型的推理过程分解为两步：首先在思考阶段，发现共享于给定任务所有实例的任务级逻辑，然后用伪代码表达逻辑；然后在执行阶段，进一步针对每个实例调整生成的伪代码并模拟代码的执行。通过对七个算法推理任务进行大量实验证明了 Think-and-Execute 的有效性。相较于执行特定实例推理的强基线（例如 CoT 和 PoT），我们的方法更好地提高了语言模型的推理能力，这表明发现任务级逻辑的帮助性。此外，我们还展示了相对于自然语言，伪代码可以更好地引导语言模型的推理，即使它们经过自然语言指导的训练。

语言模型作为编译器：模拟伪代码执行提升语言模型的算法推理能力

Language Models as Compilers: Simulating Pseudocode Execution Improves  Algorithmic Reasoning in Language Models

The conversion of user epics or stories into their appropriate representation
in pseudocode or code is a time-consuming task, which can take up a large
portion of the time in an industrial project. With this research paper, we aim
to present a methodology to generate pseudocode from a given agile user story
of small functionalities so as to reduce the overall time spent on the
industrial project. Pseudocode is a programming language agnostic
representation of the steps involved in a computer program, which can be easily
converted into any programming language. Leveraging the potential of Natural
Language Processing, we want to simplify the development process in
organizations that use the Agile Model of Software Development. We present a
methodology to convert a problem described in the English language into
pseudocode. This methodology divides the Text to Pseudocode conversion task
into two stages or subtasks, each of which is treated like an individual
machine translation task. Stage 1 is Text to Code Conversion and Stage 2 is
Code to Pseudocode Conversion. We find that the CodeT5 model gives the best
results in terms of BLEU score when trained separately on the two subtasks
mentioned above. BLEU score is a metric that is used to measure the similarity
between a machine-translated text and a set of reference translations.

使用自然语言处理，我们提出了一种从敏捷用户故事中生成伪代码的方法，以减少工业项目中花费的总时间。

利用 Transformer 将 Epic/Story 转换为伪代码

Converting Epics/Stories into Pseudocode using Transformers

We consider the task of mapping pseudocode to long programs that are
functionally correct. Given test cases as a mechanism to validate programs, we
search over the space of possible translations of the pseudocode to find a
program that passes the validation. However, without proper credit assignment
to localize the sources of program failures, it is difficult to guide search
toward more promising programs. We propose to perform credit assignment based
on signals from compilation errors, which constitute 88.7% of program failures.
Concretely, we treat the translation of each pseudocode line as a discrete
portion of the program, and whenever a synthesized program fails to compile, an
error localization method tries to identify the portion of the program
responsible for the failure. We then focus search over alternative translations
of the pseudocode for those portions. For evaluation, we collected the SPoC
dataset (Search-based Pseudocode to Code) containing 18,356 programs with
human-authored pseudocode and test cases. Under a budget of 100 program
compilations, performing search improves the synthesis success rate over using
the top-one translation of the pseudocode from 25.6% to 44.7%.

使用编译错误信号进行归因分配的伪代码到程序的综合。