Papers with unconditional generation
Generalization in Generation: A closer look at Exposure Bias (D19-56)
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| Challenge: | Autoregressive generative models are often criticized for using ground-truth contexts at training time but generated ones at test time. |
| Approach: | They propose that generalization is the underlying property to address and propose unconditional generation as its fundamental benchmark. |
| Outcome: | The proposed model is generalized and can handle true and generated contexts. |
GLM: General Language Model Pretraining with Autoregressive Blank Infilling (2022.acl-long)
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| Challenge: | Existing pretraining frameworks do not perform well for all tasks of three main categories, such as natural language understanding (NLU), unconditional generation, and conditional generation. |
| Approach: | They propose a general language model based on autoregressive blank infilling to address this challenge. |
| Outcome: | The proposed model outperforms BERT, T5, and GPT on a wide range of tasks across NLU, conditional and unconditional generation tasks. |
PromptEHR: Conditional Electronic Healthcare Records Generation with Prompt Learning (2022.emnlp-main)
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| Challenge: | Existing methods for generating longitudinal multimodal EHRs are limited due to privacy concerns. |
| Approach: | They propose to generate longitudinal multimodal EHRs by unconditional generation or longitudinal inference . existing methods generate single-modal E HRs by conditional generation or by longitudinal inferment . |
| Outcome: | The proposed method is more flexible and controllable than existing methods and is more cost-effective than existing ones. |
Multichannel Generative Language Model: Learning All Possible Factorizations Within and Across Channels (2020.findings-emnlp)
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| Challenge: | MGLM is a generative joint distribution model over channels. |
| Approach: | They propose a multichannel generative joint distribution model over channels that marginalizes over all possible factorizations within and across all channels. |
| Outcome: | The proposed model outperforms traditional bilingual discriminative models. |
MolSafeEval: A Benchmark for Uncovering Safety Risks in AI-Generated Molecules (2026.findings-acl)
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| Challenge: | generative models may produce molecules with toxic, reactive, or otherwise hazardous characteristics. |
| Approach: | They propose a benchmark to evaluate and analyze the safety risks of molecular generation. |
| Outcome: | The proposed benchmark aims to evaluate and analyze the safety risks of molecular generation. |