8 papers
Reasoning4Sciences: Bridging Reasoning Language Models to All Scientific Branches
Teddy Ferdinan, BartÅomiej Koptyra, MikoÅaj Langner +42
While Reasoning Language Models (RLMs) are rapidly emerging as powerful tools for scientific research, their impact is primarily concentrated in "hard science" fields. The slow --…
What properties of reasoning supervision are associated with improved downstream model quality?
MikoÅaj Langner, Dzmitry Pihulski, Jan Eliasz +5
Validating training data for reasoning models typically requires expensive trial-and-error fine-tuning cycles. In this work, we investigate whether the utility of a reasoning datas…
The PLLuM Instruction Corpus
Piotr PÄzik, Filip Å»arnecki, Konrad KaczyÅski +50
This paper describes the instruction dataset used to fine-tune a set of transformer-based large language models (LLMs) developed in the PLLuM (Polish Large Language Model) project.…
PLLuM: A Family of Polish Large Language Models
Jan KocoÅ, Maciej Piasecki, Arkadiusz Janz +96
Large Language Models (LLMs) play a central role in modern artificial intelligence, yet their development has been primarily focused on English, resulting in limited support for ot…
AggTruth: Contextual Hallucination Detection using Aggregated Attention Scores in LLMs
Piotr Matys, Jan Eliasz, Konrad KieÅczyÅski +4
In real-world applications, Large Language Models (LLMs) often hallucinate, even in Retrieval-Augmented Generation (RAG) settings, which poses a significant challenge to their depl…
Crowdsource, Crawl, or Generate? Creating SEA-VL, a Multicultural Vision-Language Dataset for Southeast Asia
Samuel Cahyawijaya, Holy Lovenia, Joel Ruben Antony Moniz +89
Southeast Asia (SEA) is a region of extraordinary linguistic and cultural diversity, yet it remains significantly underrepresented in vision-language (VL) research. This often resu…