3 citations · 6 across the 8 of their papers we have counts for
8 papers
Adaptive Layer Selection for Efficient Vision Transformer Fine-Tuning
Alessio Devoto, Federico Alvetreti, Jary Pomponi +3
Recently, foundation models based on Vision Transformers (ViTs) have become widely available. However, their fine-tuning process is highly resource-intensive, and it hinders their…
Attention Is All You Need But You Don't Need All Of It For Inference of Large Language Models
Georgy Tyukin, Gbetondji J-S Dovonon, Jean Kaddour +1
The inference demand for LLMs has skyrocketed in recent months, and serving models with low latencies remains challenging due to the quadratic input length complexity of the attent…
Probing the Emergence of Cross-lingual Alignment during LLM Training
Hetong Wang, Pasquale Minervini, Edoardo M. Ponti
Multilingual Large Language Models (LLMs) achieve remarkable levels of zero-shot cross-lingual transfer performance. We speculate that this is predicated on their ability to align…
Edinburgh Clinical NLP at SemEval-2024 Task 2: Fine-tune your model unless you have access to GPT-4
Aryo Pradipta Gema, Giwon Hong, Pasquale Minervini +2
The NLI4CT task assesses Natural Language Inference systems in predicting whether hypotheses entail or contradict evidence from Clinical Trial Reports. In this study, we evaluate v…
Answerability in Retrieval-Augmented Open-Domain Question Answering
Rustam Abdumalikov, Pasquale Minervini, Yova Kementchedjhieva
The performance of Open-Domain Question Answering (ODQA) retrieval systems can exhibit sub-optimal behavior, providing text excerpts with varying degrees of irrelevance. Unfortunat…
Can LLMs Correct Physicians, Yet? Investigating Effective Interaction Methods in the Medical Domain
Burcu Sayin, Pasquale Minervini, Jacopo Staiano +1
We explore the potential of Large Language Models (LLMs) to assist and potentially correct physicians in medical decision-making tasks. We evaluate several LLMs, including Meditron…