The Hitchhiker's Guide to LDA

《智能体AI的搭车指南：从基础到系统》

The Hitchhiker's Guide to Agentic AI: From Foundations to Systems

本文为构建自主AI系统提供全面实践参考，覆盖从Transformer架构、训练优化到对齐、推理、检索增强生成、记忆系统、智能体设计模式及多智能体协调的完整技术栈。

Source-LDA: Enhancing probabilistic topic models using prior knowledge sources

A popular approach to topic modeling involves extracting co-occurring n-grams of a corpus into semantic themes. The set of n-grams in a theme represents an underlying topic, but most topic modeling approaches are not able to label these sets of words with a single n-gram. Such labels are useful for topic identification in summarization systems. This paper introduces a novel approach to labeling a group of n-grams comprising an individual topic. The approach taken is to complement the existing topic distributions over words with a known distribution based on a predefined set of topics. This is …

Keyword Assisted Embedded Topic Model

By illuminating latent structures in a corpus of text, topic models are an essential tool for categorizing, summarizing, and exploring large collections of documents. Probabilistic topic models, such as latent Dirichlet allocation (LDA), describe how words in documents are generated via a set of latent distributions called topics. Recently, the Embedded Topic Model (ETM) has extended LDA to utilize the semantic information in word embeddings to derive semantically richer topics. As LDA and its extensions are unsupervised models, they aren't defined to make efficient use of a user's prior knowl…

讲述，而非展示：利用语言模型的抽象复述来建模文学主题

Tell, Don't Show: Leveraging Language Models' Abstractive Retellings to Model Literary Themes

针对传统词袋方法在文学主题建模上的不足，提出 Retell 方法，利用生成式语言模型将叙事文本转化为抽象概念，再结合 LDA 提取更精确的主题，并在文化分析案例中验证了其有效性。

带有 LLM-in-the-Loop 的语义增强潜在主题建模

Semantic-Augmented Latent Topic Modeling with LLM-in-the-Loop

本文探讨了将 LLMs 整合到 LDA 的初始化和后校正阶段以增强主题模型，发现 LLM 引导的初始化无效，但 LLM 后校正使连贯性提升 5.86%，揭示了 LLM-in-the-Loop 的实际益处并挑战了 LLM 始终更优的信念。