Papers with lightweight network
Pre-train and Plug-in: Flexible Conditional Text Generation with Variational Auto-Encoders (2020.acl-main)
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| Challenge: | Existing conditional generation models cannot handle emerging conditions due to their joint end-to-end learning fashion. |
| Approach: | They propose a framework for conditional text generation that decouples the text generation module from the condition representation module to allow "one-to-many" conditional generation. |
| Outcome: | The proposed framework decouples the text generation module from the condition representation module to allow “one-to-many” conditional generation. |
Identifying and Mitigating Social Bias Knowledge in Language Models (2025.findings-naacl)
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| Challenge: | Existing methods for debiasing may generate incorrect or nonsensical predictions but leave aside individual commonsense facts, resulting in modified knowledge that elicits unreasonable or undesired predictions. |
| Approach: | They propose a framework that identifies encoding locations of biases within language models and then applies the Fairness-Stamp (FAST) they also propose 'BiaScope' to evaluate the retention of commonsense knowledge and generalization across paraphrased social biase. |
| Outcome: | The proposed framework surpasses state-of-the-art baselines with superior debiasing performance while not compromising the overall model capability for knowledge retention and prediction. |