Summary of Assessing Generalization For Subpopulation Representative Modeling Via In-context Learning, by Gabriel Simmons and Vladislav Savinov
Assessing Generalization for Subpopulation Representative Modeling via In-Context Learning
by Gabriel Simmons, Vladislav Savinov
First submitted to arxiv on: 12 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); 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 This study investigates the ability of Large Language Model (LLM)-based Subpopulation Representative Models (SRMs) to generalize from empirical data, using in-context learning with datasets from the 2016 and 2020 American National Election Studies. The researchers explore how SRMs perform across different response variables and demographic subgroups. While conditioning with empirical data improves overall performance, the benefits of in-context learning vary significantly across demographics, sometimes hindering performance for one group while improving it for others. This uneven impact presents a challenge for practitioners implementing SRMs and decision-makers relying on them. The study highlights the need for fine-grained benchmarks that capture diversity and test not just fidelity but generalization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how big language models can be used to represent different groups of people. They use data from two American elections to see if these models can learn to make predictions about new, unseen groups. The researchers found that the model gets better when it’s trained on real-world data, but the improvement varies a lot depending on which group is being predicted. Sometimes the model does worse for one group and better for another. This makes it tricky for people who want to use these models in real-life situations. The study suggests that we need more detailed tests of how well these models work with different groups. |
Keywords
* Artificial intelligence * Generalization * Large language model
