BOLT: Fast Energy-based Controlled Text Generation with Tunable Biases (2023.acl-short)
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| Challenge: | Energy-based models (EBMs) have gained popularity for controlled text generation due to their high applicability to a wide range of constraints. |
| Approach: | They propose a language model with tunable biases to adjust the language model’s output logits. |
| Outcome: | The proposed model maintains the generator’s autoregressive nature to assert a strong control on token-wise conditional dependencies and overall fluency, and converges faster. |
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| Challenge: | Recent work on controlled text generation has required attribute-based fine-tuning of the base language model or restricted the parameterization of the attribute discriminator. |
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A Plug-and-Play Method for Controlled Text Generation (2021.findings-emnlp)
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| Challenge: | Existing methods for controlling language generation are not able to produce fluent text . current methods require additional models or fine-tuning to ensure specific words are included . |
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