most citedAttention Is All You Need But You Don't Need All Of It For Inference of Large Language Models

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cs.CL20241 cited

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…

cs.CL2024

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…

cs.CL2024

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…

cs.CL20241 cited

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…

cs.CL2023

REFER: An End-to-end Rationale Extraction Framework for Explanation Regularization

Mohammad Reza Ghasemi Madani, Pasquale Minervini

Human-annotated textual explanations are becoming increasingly important in Explainable Natural Language Processing. Rationale extraction aims to provide faithful (i.e., reflective…