5 papers
Language Models Learn Universal Representations of Numbers and Here's Why You Should Care
Michal Å tefánik, Timothee Mickus, Marek KadlÄÃk +7
Prior work has shown that large language models (LLMs) often converge to accurate input embedding for numbers, based on sinusoidal representations. In this work, we quantify that t…
TrackList: Tracing Back Query Linguistic Diversity for Head and Tail Knowledge in Open Large Language Models
Ioana Buhnila, Aman Sinha, Mathieu Constant
Large Language Models (LLMs) have proven efficient in giving definition-type answers to user input queries. While for humans giving various types of answers, such as examples and p…
ImmunoFOMO: Are Language Models missing what oncologists see?
Aman Sinha, Bogdan-Valentin Popescu, Xavier Coubez +2
Language models (LMs) capabilities have grown with a fast pace over the past decade leading researchers in various disciplines, such as biomedical research, to increasingly explore…
SemEval-2025 Task 3: Mu-SHROOM, the Multilingual Shared Task on Hallucinations and Related Observable Overgeneration Mistakes
Raúl Vázquez, Timothee Mickus, Elaine Zosa +15
We present the Mu-SHROOM shared task which is focused on detecting hallucinations and other overgeneration mistakes in the output of instruction-tuned large language models (LLMs).…
Your Model is Overconfident, and Other Lies We Tell Ourselves
Timothee Mickus, Aman Sinha, Raúl Vázquez
The difficulty intrinsic to a given example, rooted in its inherent ambiguity, is a key yet often overlooked factor in evaluating neural NLP models. We investigate the interplay an…