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.…
PLLuM: A Family of Polish Large Language Models
Jan Kocoń, 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 Kiełczyński +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…
Unraveling SITT: Social Influence Technique Taxonomy and Detection with LLMs
Wiktoria Mieleszczenko-Kowszewicz, Beata Bajcar, Aleksander Szczęsny +4
In this work we present the Social Influence Technique Taxonomy (SITT), a comprehensive framework of 58 empirically grounded techniques organized into nine categories, designed to…
From Generalized Laughter to Personalized Chuckles: Unleashing the Power of Data Fusion in Subjective Humor Detection
Julita Bielaniewicz, Przemysław Kazienko
The vast area of subjectivity in Natural Language Processing (NLP) poses a challenge to the solutions typically used in generalized tasks. As exploration in the scope of generalize…
Towards Model-Based Data Acquisition for Subjective Multi-Task NLP Problems
Kamil Kanclerz, Julita Bielaniewicz, Marcin Gruza +3
Data annotated by humans is a source of knowledge by describing the peculiarities of the problem and therefore fueling the decision process of the trained model. Unfortunately, the…
Modeling Uncertainty in Personalized Emotion Prediction with Normalizing Flows
Piotr Miłkowski, Konrad Karanowski, Patryk Wielopolski +3
Designing predictive models for subjective problems in natural language processing (NLP) remains challenging. This is mainly due to its non-deterministic nature and different perce…