Papers with downstream fine-tuning tasks
Teacher Intervention: Improving Convergence of Quantization Aware Training for Ultra-Low Precision Transformers (2023.eacl-main)
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| Challenge: | Quantization-aware training (QAT) is a promising method to lower the implementation cost and energy consumption. |
| Approach: | They propose a method for fast converging QAT of pre-trained Transformers using a layer-wise signal propagation method with the intact signal from the teacher. |
| Outcome: | The proposed method achieves superior accuracy with significantly lower fine-tuning iterations on well-known Transformers of natural language processing as well as computer vision compared to the state-of-the-art methods. |
Why LLM Safety Guardrails Collapse After Fine-tuning: A Similarity Analysis Between Alignment and Fine-tuning Datasets (2026.acl-long)
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| Challenge: | Existing mitigation strategies focus on reactively addressing jailbreak incidents after safety guardrails have been compromised. |
| Approach: | They investigate the degradation of safety guardrails through the lens of representation similarity between upstream alignment datasets and downstream fine-tuning tasks. |
| Outcome: | The proposed model reduces harmfulness score by 10.33% when compared to baseline models. |