Papers by Shuting Jiang
Layer-aware Dual-directional Modulation for Low-resource Machine Translation (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) have demonstrated excellent performance in Machine Translation (MT) however, a performance gap persists between high-resource and low-resourced languages due to imbalanced pre-training data. |
| Approach: | They propose a layer-wise metric to quantify the activation divergence between high- and low-resource languages. |
| Outcome: | The proposed model outperforms standard LoRA fine-tuning on Chinese-to-seven low-resource language translations. |
Does Large Language Model Contain Task-Specific Neurons? (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) have demonstrated remarkable capabilities in comprehensively handling various types of natural language processing (NLP) tasks. |
| Approach: | They propose a method for task-specific neuron localization based on Causal Gradient Variation with Special Tokens (CGVST) this method identifies task- specific neurons by concentrating on the most significant tokens during task processing, eliminating redundant tokens and minimizing interference from non-essential neurons. |
| Outcome: | The proposed method can locate task-specific neurons across eight public tasks. |
Breaking Consensus Bias: Unsupervised Reinforcement Learning for Machine Translation (2026.findings-acl)
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| Challenge: | Existing RL approaches for MT face fixed references or the production of homogeneous references leading to mode collapse in unsupervised settings. |
| Approach: | They propose an Entropy-Driven Unsupervised RL framework for machine translation that leverages entropy for supervision construction and self-evolution. |
| Outcome: | The proposed framework outperforms supervised and unsupervised baselines in multiple language pairs. |