Investigating Model Compression for Neural Machine Translation in the Biomedical Domain
cs.CL, cs.AI, cs.LG
Submitted: 2026-10-04
Updated: 2026-10-04
Comments: Accepted at AICS 2025
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Sources
- MCC-KD: Multi-CoT Consistent Knowledge Distillation
- QLoRA: Efficient Finetuning of Quantized LLMs
- Non-Autoregressive Neural Machine Translation
- Distilling the Knowledge in a Neural Network
- Large Language Models Are Reasoning Teachers
- Deep Encoder, Shallow Decoder: Reevaluating Non-autoregressive Machine Translation
- Quantifying the Carbon Emissions of Machine Learning
- Turning Dust into Gold: Distilling Complex Reasoning Capabilities from LLMs by Leveraging Negative Data
- Mind's Mirror: Distilling Self-Evaluation Capability and Comprehensive Thinking from Large Language Models
- A White Paper on Neural Network Quantization
- A Call for Clarity in Reporting BLEU Scores
- ZeRO: Memory Optimizations Toward Training Trillion Parameter Models
- DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
- Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference
- BLEURT: Learning Robust Metrics for Text Generation
- ReAct: Synergizing Reasoning and Acting in Language Models
- BERTScore: Evaluating Text Generation with BERT
- Understanding Knowledge Distillation in Non-autoregressive Machine Translation
- Scalable Prompt Generation for Semi-supervised Learning with Language Models
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