OmniThink: Expanding Knowledge Boundaries in Machine Writing through Thinking
- Published
- Source
- arXiv
- Paper number
- 020
- Field
- Long-form Generation
- arXiv ID
- 2501.09751
Key points
- It proposes a method that expands reflection before generation by leveraging graphs of related concepts.
- This enables the model to produce longer, more interesting, and more informative responses.
- Machine writing with large language models often relies on retrieval-augmented generation.
- However, such approaches keep the model within a preselected boundary, which limits the generation of richly informative content.
- More specifically, simply retrieved information tends to lack depth and novelty and often suffers from redundancy, which harms generated text quality and leads to shallow, unoriginal, and repetitive output.
- To address these issues, it proposes OmniThink, a slow-thinking machine writing framework that mimics the human process of iterative expansion and reflection.
Paper links
External research summaries. These are not HDATF publications or measured product results.