The paper presents a methodology for uncovering knowledge gaps on the
internet using the Retrieval Augmented Generation (RAG) model. By simulating
user search behaviour, the RAG system identifies and addresses gaps in
information retrieval systems. The study demonstrates the effectiveness of the
RAG system in generating relevant suggestions with a consistent accuracy of
93%. The methodology can be applied in various fields such as scientific
discovery, educational enhancement, research development, market analysis,
search engine optimisation, and content development. The results highlight the
value of identifying and understanding knowledge gaps to guide future
endeavours.

利用检索增强生成（RAG）模型揭示互联网上的知识空白的方法论，并通过模拟用户搜索行为，RAG 系统识别和解决信息检索系统中的空白。该研究展示了 RAG 系统在生成相关建议方面的有效性，准确率达到 93％。该方法可以应用于科学发现、教育增强、研究发展、市场分析、搜索引擎优化和内容开发等各个领域。研究结果突出了识别和理解知识空白以指导未来努力的价值。

利用检索增强生成（RAG）揭示知识缺口

Harnessing Retrieval-Augmented Generation (RAG) for Uncovering Knowledge  Gaps

We present LLMSTEP, a tool for integrating a language model into the Lean
proof assistant. LLMSTEP is a Lean 4 tactic that sends a user's proof state to
a server hosting a language model. The language model generates suggestions,
which are checked in Lean and displayed to a user in their development
environment. We provide a baseline language model, along with code for
fine-tuning and evaluation to support further development. We provide server
implementations that run on CPU, a CUDA GPU, or a Google Colab notebook, as a
step towards fast, effective language model suggestions for any user.

LLMSTEP 是将语言模型整合到 Lean 证明助手中的工具，它将用户的证明状态发送到托管语言模型的服务器，语言模型生成建议并在 Lean 中检查后显示给用户在其开发环境中。我们提供了基准语言模型，以及用于微调和评估的代码，以支持进一步的开发。我们提供了在 CPU、CUDA GPU 或 Google Colab 笔记本上运行的服务器实现，这是快速有效的语言模型建议的一小步。

LLMSTEP: Lean 中的 LLM 证明步骤建议

LLMSTEP: LLM proofstep suggestions in Lean

We consider interactive tools that help users search for their most preferred
item in a large collection of options. In particular, we examine
example-critiquing, a technique for enabling users to incrementally construct
preference models by critiquing example options that are presented to them. We
present novel techniques for improving the example-critiquing technology by
adding suggestions to its displayed options. Such suggestions are calculated
based on an analysis of users current preference model and their potential
hidden preferences. We evaluate the performance of our model-based suggestion
techniques with both synthetic and real users. Results show that such
suggestions are highly attractive to users and can stimulate them to express
more preferences to improve the chance of identifying their most preferred item
by up to 78%.

本文探讨了交互式工具＂例子批判＂来帮助用户在大量选项中快速找到最合适的物品，并介绍了通过基于用户当前的喜好模型和潜在的隐藏喜好来计算建议来改善该技术的新技术。用合成和真实用户进行性能评估，结果表明，这些建议对用户非常吸引人，可以刺激他们表达更多的偏好以提高识别出最喜欢的物品的机会，提高了 78%。