Summary of Composable Interventions For Language Models, by Arinbjorn Kolbeinsson et al.
Composable Interventions for Language Models
by Arinbjorn Kolbeinsson, Kyle O’Brien, Tianjin Huang, Shanghua Gao, Shiwei Liu, Jonathan Richard Schwarz, Anurag Vaidya, Faisal Mahmood, Marinka Zitnik, Tianlong Chen, Thomas Hartvigsen
First submitted to arxiv on: 9 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 framework for studying the effects of combining multiple test-time interventions on language models is introduced. The proposed composable interventions approach allows researchers to analyze how different types of interventions, such as knowledge editing, model compression, and machine unlearning, interact when applied sequentially. This framework features new metrics and a unified codebase, which enables the composition of popular methods from various intervention categories. Extensive experiments were conducted using 310 different compositions, revealing meaningful interactions between interventions. The results highlight the importance of considering the order of application and the limitations of general-purpose metrics in assessing composability. The study suggests a need for new multi-objective interventions to address gaps in composability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Language models can be improved by applying certain “interventions” without retraining them. However, these interventions are often developed separately and applied together, which makes it hard to understand how they work together. To solve this problem, researchers introduced a new way of combining multiple interventions on the same language model. They called this approach “composable interventions.” The framework includes new metrics and code that allows different methods from various intervention categories to be combined. By testing 310 different combinations, the study found that some interventions work better together than others. It also showed that popular metrics are not good enough for understanding how these interventions work together. Overall, the findings suggest that there is a need for new ways of combining multiple interventions. |
Keywords
* Artificial intelligence * Language model * Model compression
