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| ||<style="border:0;padding-top:5px;padding-bottom:5px">'''15 September 2025'''|| ||<style="border:0;padding-left:30px;padding-bottom:0px">'''Louis Esteve''' (Universite Paris-Saclay) || ||<style="border:0;padding-left:30px;padding-bottom:5px">'''[[attachment:seminarium-archiwum/2025-09-15.pdf|Diversity and dataset size – a quantitative perspective]]'''  {{attachment:seminarium-archiwum/icon-en.gif|Talk in English.}}|| ||<style="border:0;padding-left:30px;padding-bottom:15px">The field of Natural Language Processing (NLP) studies the abilities of computer systems to process and generate natural language, and has received increasing attention from the general population since the democratisation of generative and conversational models. However, behind the scenes, state-of-the-art NLP models are trained on ever-larger datasets, reaching trillions of tokens. It may be argued that the creation and use of such immense datasets is motivated by the idea that 'the larger the dataset, the more diverse it is', and that in turn 'if the training set is more diverse, it shall yield better models'. However, these statements thus far remain intuitions and need to be properly tested. To this end, this presentation will tackle methods and caveats of formal diversity quantification including limitations of the literature, a preliminary discussion on the link between diversity and dataset size, as well as their impact on downstream applications.|| ||<style="border:0;padding-top:5px;padding-bottom:5px">'''6 October 2025'''|| ||<style="border:0;padding-left:30px;padding-bottom:0px">'''Stan Matwin''' (Dalhousie University / Institute of Computer Science, Polish Academy of Sciences) || ||<style="border:0;padding-left:30px;padding-bottom:5px">[[http://zil.ipipan.waw.pl/seminarium-online|{{attachment:seminarium-archiwum/teams.png}}]] '''[[attachment:seminarium-archiwum/2025-10-06.pdf|Deep, multi-faceted learning of diagnosing mental disorders from clinical interview records]]'''  {{attachment:seminarium-archiwum/icon-pl.gif|Talk in Polish.}}{{attachment:seminarium-archiwum/icon-en.gif|Slides partially in English.}}|| ||<style="border:0;padding-left:30px;padding-bottom:15px">The key characteristics of mental illnesses are reflected in audio recordings of clinical interviews with patients and their families. We have developed a deep learning method that automatically extracts the relevant features necessary for the diagnosis of mental illnesses (ADHD, depression, bipolar disorder and schizophrenia) from such interviews. We use a variety of pre-trained models to extract representations from both the audio segments of these interviews and their text versions. We use several modern representation techniques (embeddings). We apply a Big Data approach by exploring existing audio and text corpora annotated with emotional labels. We address the problem of annotated data scarcity by using parametric model fine-tuning (Parameter Efficient Fine-Tuning). All these representations are then combined into a single multimodal form. To diagnose the above mental disorders, we use contrastive learning and model synthesis using a committee of experts (Mixture of Experts). The results show that through multimodal analysis of clinical interviews, mental disorders can be diagnosed with satisfactory accuracy (project conducted in collaboration with H. Naderi and R. Uher).|| ||<style="border:0;padding-top:5px;padding-bottom:5px">'''20 October 2025'''|| ||<style="border:0;padding-left:30px;padding-bottom:0px">'''Arkadiusz Modzelewski''' || ||<style="border:0;padding-left:30px;padding-bottom:5px">'''The Why and How of Disinformation: Datasets, Methods and Language Models Evaluation'''  {{attachment:seminarium-archiwum/icon-en.gif|Talk in English.}}|| ||<style="border:0;padding-left:30px;padding-bottom:15px">What language tools do disinformation agents employ? Can incorporating persuasion and intent knolwedge enhance the ability of large language models to detect disinformation? And how effective are LLMs at identifying disinformation in Polish and English? In this talk, I will present findings from my PhD research on disinformation, persuasion, and the intent behind misleading information. I will introduce one of the largest Polish disinformation datasets, alongside a novel English dataset, both designed to capture manipulative techniques and intent of disinformation agents. Drawing on these and other resources, I will discuss how well current LLMs perform in detecting disinformation, persuasion, and intent, and highlight promising directions for improving their effectiveness in disinformation detection..|| ||<style="border:0;padding-top:5px;padding-bottom:5px">'''3 November 2025'''|| ||<style="border:0;padding-left:30px;padding-bottom:0px">'''Gražina Korvel''' (Vilnius University) || ||<style="border:0;padding-left:30px;padding-bottom:5px">'''Talk title will be given soon'''  {{attachment:seminarium-archiwum/icon-pl.gif|Talk in Polish.}}|| ||<style="border:0;padding-left:30px;padding-bottom:15px">Talk summary wiil be made available shortly.|| ||<style="border:0;padding-top:10px">Please see also [[http://nlp.ipipan.waw.pl/NLP-SEMINAR/previous-e.html|the talks given in 2000–2015]] and [[http://zil.ipipan.waw.pl/seminar-archive|2015–2025]].|| |
||<style="border:0;padding-top:10px">Please see also [[http://nlp.ipipan.waw.pl/NLP-SEMINAR/previous-e.html|the talks given in 2000–2015]] and [[http://zil.ipipan.waw.pl/seminar-archive|2015–2026]].|| |
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||<style="border:0;padding-top:5px;padding-bottom:5px">'''17 November 2025''' '''(NOTE: the seminar will start at 16:00)'''|| ||<style="border:0;padding-left:30px;padding-bottom:0px">'''Marzena Karpińska''' (Microsoft) || ||<style="border:0;padding-left:30px;padding-bottom:5px">[[http://zil.ipipan.waw.pl/seminarium-online|{{attachment:seminarium-archiwum/teams.png}}]] '''!OneRuler: testing multilingual language models on long contexts'''  {{attachment:seminarium-archiwum/icon-pl.gif|Talk in Polish}}|| ||<style="border:0;padding-left:30px;padding-bottom:15px">In this presentation, I will look at how well language models perform when extracting information from texts of up to 128,000 tokens (approximately 100,000 words) in 26 languages, including Polish. The results of the experiments show that as the length of the context increases, the differences between languages with large and small data resources also increase. Surprisingly, even minimal changes in the command (adding the possibility that the information does not exist) cause a significant decrease in effectiveness, especially with longer texts.|| |
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||<style="border:0;padding-top:5px;padding-bottom:5px">'''2 April 2020'''|| ||<style="border:0;padding-left:30px;padding-bottom:0px">'''Stan Matwin''' (Dalhousie University)|| ||<style="border:0;padding-left:30px;padding-bottom:5px">'''Efficient training of word embeddings with a focus on negative examples'''  {{attachment:seminarium-archiwum/icon-pl.gif|Talk delivered in Polish.}} {{attachment:seminarium-archiwum/icon-en.gif|Slides in English.}}|| ||<style="border:0;padding-left:30px;padding-bottom:15px">This presentation is based on our [[https://pdfs.semanticscholar.org/1f50/db5786913b43f9668f997fc4c97d9cd18730.pdf|AAAI 2018]] and [[https://aaai.org/ojs/index.php/AAAI/article/view/4683|AAAI 2019]] papers on English word embeddings. In particular, we examine the notion of “negative examples”, the unobserved or insignificant word-context co-occurrences, in spectral methods. we provide a new formulation for the word embedding problem by proposing a new intuitive objective function that perfectly justifies the use of negative examples. With the goal of efficient learning of embeddings, we propose a kernel similarity measure for the latent space that can effectively calculate the similarities in high dimensions. Moreover, we propose an approximate alternative to our algorithm using a modified Vantage Point tree and reduce the computational complexity of the algorithm with respect to the number of words in the vocabulary. We have trained various word embedding algorithms on articles of Wikipedia with 2.3 billion tokens and show that our method outperforms the state-of-the-art in most word similarity tasks by a good margin. We will round up our discussion with some general thought s about the use of embeddings in modern NLP.|| |
Natural Language Processing Seminar 2025–2026
The NLP Seminar is organised by the Linguistic Engineering Group at the Institute of Computer Science, Polish Academy of Sciences (ICS PAS). It will restart in October and will take place on (some) Mondays, usually at 10:15 am, often online – please use the link next to the presentation title. All recorded talks are available on YouTube. |
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Please see also the talks given in 2000–2015 and 2015–2026. |
