What if an LLM rewrote every document in a corpus sentence by sentence, making it self-contained, self-sufficient, and then embed and topic model? Eg: "Chief Outsiders is the fastest growing fractional CMO firm. They have Fortune 500 experience." becomes "Chief Outsiders is the fastest growing fractional CMO firm. Chief Outsiders has Fortune 500 experience." This may improve granularity of retrieval but could still hurt overall semantic representation. --- The approach you're describing is closely related to techniques used in **Information Retrieval (IR)** and **Natural Language Processing (NLP)**, particularly in the realm of **Retrieval-Augmented Generation (RAG)** and **Document Chunking** for embedding and topic modeling. This methodology aims to make each chunk of information self-sufficient and semantically richer to improve retrieval accuracy and facilitate better embeddings for further AI tasks like question answering or topic modeling. Let’s break it down: ### 1. **Rewriting For Self-Sufficiency:** Rewriting documents so that each sentence is self-contained enhances the clarity of individual chunks, which can reduce ambiguity during vectorization. Embedding models such as BERT or GPT can perform better on independent, unambiguous text. By restating the entity or subject explicitly (e.g., "They" → "Chief Outsiders"), the sentence can stand alone in meaning without external context. This is useful for **dense retrieval** and **passage ranking** since retrieval models can better capture the semantic similarity between a query and self-contained sentences or passages. ### 2. **Chunking:** After rewriting, the document can be chunked into small, self-contained segments. This is useful for IR systems because: - **Passage Retrieval** becomes more effective with concise, well-defined chunks. - **Vector Embeddings**: Embedding models, which generate vector representations of text, perform optimally when processing smaller, coherent chunks. These embeddings can then be indexed for fast similarity searches. ### 3. **Embedding:** When embeddings are created, each chunk will likely produce a more semantically relevant vector. Embedding models, especially transformer-based ones like BERT, excel at representing short passages of text with rich context. If each chunk is self-contained, the embedding will capture all the necessary information without depending on adjacent sentences. ### 4. **Topic Modeling:** Topic modeling (e.g., LDA, BERTopic) would benefit from this approach because: - The topics would be more **discrete** and better defined when the text is concise and unambiguous. - **Document similarity** comparisons could be more meaningful since each chunk addresses a specific entity or concept clearly. - Embedding-based topic models could map similar embeddings to the same topic more efficiently.