Research map / Image generation / Text-to-image diffusion
Tractable Control for Autoregressive Language Generation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use GeLaTo with tractable HMM constraint guidance instead of A*esque decoding | GPT2-large on CommonGen, unsupervised · CommonGen test | BLEU-4: 28.6 -> 29.0 | Helped |
| Use GeLaTo with tractable HMM constraint guidance instead of A*esque decoding | GPT2-large on CommonGen, supervised · CommonGen test | BLEU-4: 28.2 -> 34.1 | Helped |
| Use GeLaTo rather than NADO | Yelp!Review · Yelp!Review | BLEU-4: 6.0 -> 6.6 | Helped |
| Use GeLaTo rather than InsNet | News · News | BLEU-4: 5.0 -> 5.4 | Helped |
| Increase beam size (ablation) | GeLaTo on CommonGen · CommonGen dev | BLEU | Helped |
| Vary the supervised mixture weight w across 0.1 to 0.8 (ablation) | GeLaTo on CommonGen · CommonGen dev | BLEU | No clear effect |
| Improve the HMM approximation of the base language model through additional distillation training (ablation) | GeLaTo · CommonGen | BLEU | Helped |
Closest papers by meaning
- Guaranteed Generation from Large Language Models (ICLR 2025 · similarity 0.90)
- Controllable Generation via Locally Constrained Resampling (ICLR 2025 · similarity 0.90)
- Syntactic and Semantic Control of Large Language Models via Sequential Monte Carlo (ICLR 2025 · similarity 0.90)
- Beyond Autoregression: Fast LLMs via Self-Distillation Through Time (ICLR 2025 · similarity 0.88)
- Reflection-Window Decoding: Text Generation with Selective Refinement (ICML 2025 · similarity 0.88)
- Flexible Language Modeling in Continuous Space with Transformer-based Autoregressive Flows (NeurIPS 2025 · similarity 0.87)
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