Summary of Language Models As Causal Effect Generators, by Lucius E.j. Bynum and Kyunghyun Cho
Language Models as Causal Effect Generators
by Lucius E.J. Bynum, Kyunghyun Cho
First submitted to arxiv on: 12 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Applications (stat.AP); Methodology (stat.ME); Machine Learning (stat.ML)
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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 framework uses large language models (LLMs) to generate data with controllable causal structures. This is achieved by defining a procedure to transform LLMs and directed acyclic graphs (DAGs) into sequence-driven structural causal models (SD-SCMs). SD-SCMs are causal models that combine user-defined structure with LLM-defined structural equations, allowing for sampling from observational, interventional, and counterfactual distributions. The framework also enables the creation of a new type of benchmark for causal inference methods, generating individual-level counterfactual data without requiring manual specification of functional relationships between variables. The proposed method is demonstrated by creating an example benchmark consisting of thousands of datasets and testing popular estimation methods on these datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a way to use big language models to create fake data that follows certain rules or patterns. These rules are controlled by the model, allowing for different types of data generation. The researchers tested this method on various tasks, including estimating average and individual treatment effects, with and without hidden confounding. This method can be useful in auditing language models to detect misinformation, bias, or other unwanted behaviors. |
Keywords
* Artificial intelligence * Inference
