Yasmin Moslem

I build and compress multilingual, multimodal systems, and make them cheap to run at inference time.

Research Fellow at the ADAPT Centre, School of Computer Science, Trinity College Dublin. My work covers multi-LLM routing and cascades, iterative layer pruning, knowledge distillation, and quantization, applied across text, speech, and translation.

Dublin, Ireland

Selected work

Efficient inference · with Huawei Research, 2026

CRE-Router, cost-aware serving across many LLMs

A two-stage routing and cascading framework. It clusters incoming queries by difficulty to pick the most cost-effective model for each one, then escalates only the outputs it is not confident in. The result nearly matches the strongest single model on accuracy while running at meaningfully lower latency.

RoutingCascadesLLM serving

Paper Code

Speech · IWSLT 2025

Halving an 8B speech translation model

Compressed Qwen2-Audio to half its parameters and storage by combining iterative layer pruning, 4-bit QLoRA, and knowledge distillation, while holding on to almost all of the teacher model's quality.

50% fewer parameters 97 to 100% of teacher quality

Paper

Model compression · WMT 2025

Pruning a multilingual LLM without losing quality

Iterative layer pruning applied to Aya-Expanse 8B. Removing between a fifth and nearly half of the model left translation quality almost intact and made it substantially faster to serve, with a further speedup once deployed through vLLM.

22 to 44% smaller 99% of COMET retained 4.3× faster serving

Paper

Open models · AfricaNLP 2026, Spotlight

AfriNLLB, faster open translation for African languages

Compressed translation models covering 15 language pairs in both directions, released openly alongside a curated training set. They run considerably faster than the NLLB-200 baseline at equal or better quality.

30 directions 2.4× faster inference 1.6M open segments

Models and dataset

Models and datasets

Most of what I build ships publicly. Compressed translation and speech models, the AfriNLLB releases and their 1.6M-segment dataset, and the models and data produced through the SpeechT programme are all openly available.

Hugging Face GitHub

Publications

  1. Cluster, route, escalate: Cascaded framework for cost-aware LLM serving arXiv, 2026 Link
  2. Dynamic model routing and cascading for efficient LLM inference: A survey Transactions on Machine Learning Research (TMLR), 2026 Link
  3. Iterative layer pruning for efficient translation inference WMT 2025 Link
  4. Efficient speech translation through model compression and knowledge distillation IWSLT 2025 Link
  5. Adaptive machine translation with large language models EAMT 2023 300+ citations Link
  6. Domain-specific text generation for machine translation AMTA 2022 Best Presentation Award Link

Full list on Google Scholar

Community and service

Background

Ten years of building NLP systems across more than thirty languages, in research labs and in production.

Education