1 citations · 1 across the 7 of their papers we have counts for
9 papers
Safety for Whom? Boundary-Aware Self-Distillation for Controlled LLM Safety Refusal
Alejo López-Ávila, Iker García-Ferrero, Jezabel Garcia +2
Safety alignment is usually posed as a topic-level question: is this subject harmful? Deployments ask a narrower one. A civics tutor and a public-sector assistant may share a base…
Quantization-Aware Healing: A Practical Recipe for Recovering Compressed, 4-Bit LLMs
Bakbergen Ryskulov, Iker García-Ferrero, David Montero +5
Serving large language models cheaply increasingly means shipping models that are both structurally compressed to a fraction of their parameters and quantized to 4 bits. Together t…
Efficient Knowledge Distillation for LLMs: Offline Top-K Logits and a Fused Chunked KL Loss
Bakbergen Ryskulov, Iker García-Ferrero, David Montero +5
Small language models are often the only option for deployment under tight latency, cost, and on-premises constraints, but they are rarely trained from scratch: a compressed model…
Refusal Steering: Fine-grained Control over LLM Refusal Behaviour for Sensitive Topics
Iker García-Ferrero, David Montero, Roman Orus
We introduce Refusal Steering, an inference-time method to exercise fine-grained control over Large Language Models refusal behaviour on politically sensitive topics without retrai…
Instructing Large Language Models for Low-Resource Languages: A Systematic Study for Basque
Oscar Sainz, Naiara Perez, Julen Etxaniz +9
Instructing language models with user intent requires large instruction datasets, which are only available for a limited set of languages. In this paper, we explore alternatives to…
GuideX: Guided Synthetic Data Generation for Zero-Shot Information Extraction
Neil De La Fuente, Oscar Sainz, Iker García-Ferrero +1
Information Extraction (IE) systems are traditionally domain-specific, requiring costly adaptation that involves expert schema design, data annotation, and model training. While La…