3 citations · 5 across the 12 of their papers we have counts for
12 papers
Reasoning4Sciences: Bridging Reasoning Language Models to All Scientific Branches
Teddy Ferdinan, Bartłomiej Koptyra, Mikołaj 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 --…
IMPACTeen: Intentions, Manipulation, Persuasion, Annotations, and Consequences in Teen Communication Dataset
Aleksander Szczęsny, Wiktoria Mieleszczenko-Kowszewicz, Maciej Markiewicz +5
IMPACTeen is a dataset of textual social influence scenarios spanning interpersonal, media-based, and digital settings in an adolescent context. It contains 1,021 texts, 5,100 indi…
What properties of reasoning supervision are associated with improved downstream model quality?
Mikołaj 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…
Sociodemographic Biases in Educational Counselling by Large Language Models
Tomasz Adamczyk, Wiktoria Mieleszczenko-Kowszewicz, Beata Bajcar +6
As Large Language Models (LLMs) are increasingly integrated into educational settings, understanding their potential biases is critical. This study examines sociodemographic biases…
How Annotation Trains Annotators: Competence Development in Social Influence Recognition
Maciej Markiewicz, Beata Bajcar, Wiktoria Mieleszczenko-Kowszewicz +8
Human data annotation, especially when involving experts, is often treated as an objective reference. However, many annotation tasks are inherently subjective, and annotators' judg…
The PLLuM Instruction Corpus
Piotr Pęzik, Filip Żarnecki, Konrad Kaczyński +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.…