OmniThink: Expanding Knowledge Boundaries in Machine Writing through Thinking
Zekun Xi, Wenbiao Yin, Jizhan Fang, Jialong Wu, Runnan Fang, Ningyu Zhang, Jiang Yong, Pengjun Xie, Fei Huang, Huajun Chen
2025-01-17
Summary
This paper talks about OmniThink, a new way to make AI write better and more informative articles. It's like teaching a computer to think and learn more like a human does when writing about a topic.
What's the problem?
Current AI writing systems often use a method called retrieval-augmented generation, which is like giving the AI a bunch of information to work with. But this approach tends to produce articles that are shallow, repetitive, and not very original. It's as if the AI is just repeating facts without really understanding or exploring the topic deeply.
What's the solution?
The researchers created OmniThink, which tries to copy how humans learn and write about topics. Instead of just using the information it's given, OmniThink keeps thinking about the topic, expanding its understanding, and reflecting on what it knows. This process helps it create articles that are more informative and original. They tested OmniThink and found that it could write articles with more knowledge packed into them, while still making sense and covering topics in-depth.
Why it matters?
This matters because it could lead to AI that can write much better, more informative articles on its own. This could be really useful for things like creating educational content, writing reports, or even helping with research. It's a step towards AI that can think more creatively and deeply about topics, rather than just repeating information. This could change how we use AI for writing and information gathering in many fields.
Abstract
Machine writing with large language models often relies on retrieval-augmented generation. However, these approaches remain confined within the boundaries of the model's predefined scope, limiting the generation of content with rich information. Specifically, vanilla-retrieved information tends to lack depth, utility, and suffers from redundancy, which negatively impacts the quality of generated articles, leading to shallow, repetitive, and unoriginal outputs. To address these issues, we propose OmniThink, a machine writing framework that emulates the human-like process of iterative expansion and reflection. The core idea behind OmniThink is to simulate the cognitive behavior of learners as they progressively deepen their knowledge of the topics. Experimental results demonstrate that OmniThink improves the knowledge density of generated articles without compromising metrics such as coherence and depth. Human evaluations and expert feedback further highlight the potential of OmniThink to address real-world challenges in the generation of long-form articles.