Summary of Regulation Of Language Models with Interpretability Will Likely Result in a Performance Trade-off, by Eoin M. Kenny and Julie A. Shah
Regulation of Language Models With Interpretability Will Likely Result In A Performance Trade-Off
by Eoin M. Kenny, Julie A. Shah
First submitted to arxiv on: 12 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
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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 A novel machine learning study explores the implications of regulation on large-language models (LLMs) when integrated with human collaboration. The researchers built a regulatable LLM and investigated how additional constraints affect model performance and human task outcomes. Their findings reveal a “regulation performance trade-off” resulting in a 7.34% classification performance drop, but surprisingly, they demonstrate that these systems can improve human task speed and confidence in realistic deployment settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Regulation is an important issue in machine learning, but it’s unclear how to make this happen or what the effects would be on model performance if humans worked together. This study answers these questions by creating a large-language model that follows rules and measuring its impact on both the model and human collaboration. The results show that we can force an LLM to use specific features in a clear way, but there’s a trade-off: the model performs slightly worse. However, these systems can actually help humans complete tasks faster and more confidently when working together. |
Keywords
» Artificial intelligence » Classification » Large language model » Machine learning
