Large language models (LLMs) have already revolutionized code generation,
after being pretrained on publicly available code data. However, while various
methods have been proposed to augment LLMs with retrieved knowledge and enhance
the quality of code generation, the performance of these retrieval-based
methods is limited by the strength of the retrievers used. In addition, while
LLMs show great emergent ability, they still struggle to produce the correct
code in one turn. To address these challenges, we propose a novel two-step
pipeline, called \autoknow, that leverages LLMs as both knowledge providers and
self-reflective programmers. Unlike retrieval-based methods, \autoknow~obtains
the knowledge from input prompts and generates intermediate code based on the
generated knowledge. After that, \autoknow~asks LLM to act as an expert
programmer to perform debugging for the generated code. This is achieved by
receiving the error message from the interpreter, without requiring special
test cases for correctness verification. We evaluate \autoknow~on three code
generation datasets, including DS-1000 for data science code, HumanEval for
software engineering code, and TransCoder for C++-to-Python translation. Our
empirical experiments show that \autoknow~outperforms strong baselines by a
significant margin on all datasets. We also conduct exhaustive analytical
experiments to validate the effectiveness of the two stages of \autoknow, and
find that both are superior to other prompting-based methods. Further
scalability analysis demonstrates that \autoknow~can be adapted to other more
advanced models, such as GPT-4, and bring consistent efficacy improvement.

提出了一种名为 AutoKnow 的新型两步流程，将 LLMs 作为知识提供者和自我反思程序员，通过从输入提示中获得知识并根据生成的知识生成中间代码，并存在解释器中接收错误消息，从而使 LLMs 成为专业编程人员，从而成功完成编程，有效提高了二者的表现。

SelfEvolve：基于大语言模型的代码进化框架

SelfEvolve: A Code Evolution Framework via Large Language Models

Can one build a knowledge graph (KG) for all products in the world? Knowledge
graphs have firmly established themselves as valuable sources of information
for search and question answering, and it is natural to wonder if a KG can
contain information about products offered at online retail sites. There have
been several successful examples of generic KGs, but organizing information
about products poses many additional challenges, including sparsity and noise
of structured data for products, complexity of the domain with millions of
product types and thousands of attributes, heterogeneity across large number of
categories, as well as large and constantly growing number of products. We
describe AutoKnow, our automatic (self-driving) system that addresses these
challenges. The system includes a suite of novel techniques for taxonomy
construction, product property identification, knowledge extraction, anomaly
detection, and synonym discovery. AutoKnow is (a) automatic, requiring little
human intervention, (b) multi-scalable, scalable in multiple dimensions (many
domains, many products, and many attributes), and (c) integrative, exploiting
rich customer behavior logs. AutoKnow has been operational in collecting
product knowledge for over 11K product types.

AutoKnow 是一个自动的系统，用于构建包含在线零售网站产品信息的知识图谱。该系统使用创新技术构建分类系统、识别产品属性、提取知识、检测异常和发现同义词。