Research map / Probabilistic and scientific modeling / Causal discovery
A Causal Lens for Controllable Text Generation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add counterfactual confounder-balancing objectives for z and c (ablation) | Causal conditional generation · Yelp | control accuracy: 91.1 -> 96.3 | Helped |
| Add counterfactual confounder-balancing objectives for z and c (ablation) | Causal conditional generation · Yelp | bias: 89.2 -> 59.8 | Helped |
| Add counterfactual confounder-balancing objectives for z and c (ablation) | Causal conditional generation · Bios | control accuracy: 97.3 -> 99.2 | Helped |
| Add counterfactual confounder-balancing objectives for z and c (ablation) | Causal conditional generation · Bios | bias: 70.1 -> 62.4 | Helped |
| Add counterfactual confounder-balancing objectives for z and c (ablation) | Causal attribute transfer · Biased Yelp | control accuracy: 75.0 -> 77.0 | Helped |
| Add counterfactual confounder-balancing objectives for z and c (ablation) | Causal attribute transfer · Biased Yelp | bias: 67.8 -> 61.4 | Helped |
| Add counterfactual confounder-balancing objectives for z and c (ablation) | Causal attribute transfer · Unbiased Yelp | control accuracy: 87.1 -> 91.9 | Helped |
| Add counterfactual confounder-balancing objectives for z and c (ablation) | Causal attribute transfer · Unbiased Yelp | ref-BLEU: 24.3 -> 25.5 | Helped |
Closest papers by meaning
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- Successor Features for Efficient Multi-Subject Controlled Text Generation (ICML 2024 · similarity 0.85)
- Causality can systematically address the monsters under the bench(marks) (NeurIPS 2026 · similarity 0.85)
- Controlled Text Generation via Language Model Arithmetic (ICLR 2024 · similarity 0.84)
- SCOPE: A Self-supervised Framework for Improving Faithfulness in Conditional Text Generation (ICLR 2025 · similarity 0.84)
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