Summary of Interlocking-free Selective Rationalization Through Genetic-based Learning, by Federico Ruggeri et al.
Interlocking-free Selective Rationalization Through Genetic-based Learning
by Federico Ruggeri, Gaetano Signorelli
First submitted to arxiv on: 13 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Neural and Evolutionary Computing (cs.NE)
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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 The proposed GenSPP architecture for selective rationalization tackles the issue of suboptimal equilibrium minima, known as interlocking, in popular end-to-end pipelines. By introducing genetic global search to perform disjoint training of the generator and predictor, GenSPP achieves interlocking-free performance without requiring additional learning overhead. Experimental results on both synthetic and real-world benchmarks demonstrate its superiority over state-of-the-art competitors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to understand text is being developed! This paper talks about a problem in computers that make decisions based on what they’ve read. It’s called “interlocking” and it makes the computer get stuck in one way of thinking. The researchers created a new system, GenSPP, that avoids this problem by training its parts separately. They tested it on fake and real texts and showed that it works better than other systems. |
