Summary of A Survey Of Controllable Learning: Methods and Applications in Information Retrieval, by Chenglei Shen et al.
A Survey of Controllable Learning: Methods and Applications in Information Retrieval
by Chenglei Shen, Xiao Zhang, Teng Shi, Changshuo Zhang, Guofu Xie, Jun Xu
First submitted to arxiv on: 4 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this research paper, the authors provide a formal definition of controllable learning (CL) and discuss its applications in information retrieval (IR). The survey categorizes CL based on what is controllable, who controls, how control is implemented, and where to implement control. The authors identify challenges faced by CL across training, evaluation, task setting, and deployment in online environments. They also outline promising directions for CL in theoretical analysis, efficient computation, empowering large language models, application scenarios, and evaluation frameworks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning can help us get the right information when we need it. Sometimes our goals change or new information comes out, so we need to adapt. This paper defines a way to make machine learning systems adaptable without needing to retrain them every time. It looks at how this works in information retrieval, where we often need to find specific things quickly and efficiently. The authors group these adaptable systems into different categories based on what they can control, who controls it, and how they do it. |
Keywords
* Artificial intelligence * Machine learning
