Papers by Lucie-Aimée Kaffee
Investigating Human Values in Online Communities (2025.naacl-long)
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| Challenge: | Existing value frameworks struggle with sample sizes and rely on selfreported surveys to calculate values. |
| Approach: | They propose a method to computationally analyse values on Reddit using in-domain and out-of-domain human annotations to train a value relevance and a polarity classifier. |
| Outcome: | The proposed method can be used to analyse values on reddit using human annotations and human annotation. |
Why Should This Article Be Deleted? Transparent Stance Detection in Multilingual Wikipedia Editor Discussions (2023.emnlp-main)
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| Challenge: | Currently, only 20% of the English comments explicitly mention content moderation policies, but as few as 2% of the German and Turkish comments. |
| Approach: | They propose to use a multilingual dataset to predict stances with existing content moderation policies and to use them to explain moderation decisions. |
| Outcome: | The proposed model predicts stances and corresponding reasons with high accuracy, adding transparency to the decision-making process. |
Learning to Generate Wikipedia Summaries for Underserved Languages from Wikidata (N18-2)
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Lucie-Aimée Kaffee, Hady Elsahar, Pavlos Vougiouklis, Christophe Gravier, Frédérique Laforest, Jonathon Hare, Elena Simperl
| Challenge: | Existing Wikipedia content is unevenly distributed among 287 languages . authors propose a neural network architecture that generates textual summaries from Wikidata triples . |
| Approach: | They propose an automated approach to generate Wikipedia summaries from Wikidata triples using structured data. |
| Outcome: | The proposed approach is tested on Arabic and Esperanto languages with limited editors and content in the most under-resourced Wikipedias. |
Investigating Wit, Creativity, and Detectability of Large Language Models in Domain-Specific Writing Style Adaptation of Reddit’s Showerthoughts (2024.starsem-1)
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| Challenge: | Recent Large Language Models (LLMs) have shown the ability to generate content that is difficult or impossible to distinguish from human writing. |
| Approach: | They compare GPT-2 and GPT-Neo fine-tuned on Reddit data and GTP-3.5 invoked in a zero-shot manner, against human-authored texts. |
| Outcome: | The proposed model can generate short, creative texts that are difficult to distinguish from human writing, but human evaluators rate them worse than the model. |
Thorny Roses: Investigating the Dual Use Dilemma in Natural Language Processing (2023.findings-emnlp)
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| Challenge: | Dual use is a problem in the context of natural language processing, says aaron eliotta . eelisa et al.: it is important to examine their rightful use and potential misuse . |
| Approach: | They propose a definition and checklist for dual-use in natural language processing based on a survey of NLP researchers and practitioners. |
| Outcome: | The proposed checklist focuses on dual-use in NLP based on a survey of NLP researchers and practitioners. |
Presumed Cultural Identity: How Names Shape LLM Responses (2025.findings-emnlp)
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| Challenge: | Names can be used as markers of individuality, cultural heritage, and personal history when interacting with chatbots. |
| Approach: | They propose to use names as cultural bias in chatbots to adapt to user input and task contexts. |
| Outcome: | The proposed method demonstrates that LLMs make cultural identity assumptions based on their users’ presumed backgrounds based upon their names . |
SHADES: Towards a Multilingual Assessment of Stereotypes in Large Language Models (2025.naacl-long)
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Margaret Mitchell, Giuseppe Attanasio, Ioana Baldini, Miruna Clinciu, Jordan Clive, Pieter Delobelle, Manan Dey, Sil Hamilton, Timm Dill, Jad Doughman, Ritam Dutt, Avijit Ghosh, Jessica Zosa Forde, Carolin Holtermann, Lucie-Aimée Kaffee, Tanmay Laud, Anne Lauscher, Roberto L Lopez-Davila, Maraim Masoud, Nikita Nangia, Anaelia Ovalle, Giada Pistilli, Dragomir Radev, Beatrice Savoldi, Vipul Raheja, Jeremy Qin, Esther Ploeger, Arjun Subramonian, Kaustubh Dhole, Kaiser Sun, Amirbek Djanibekov, Jonibek Mansurov, Kayo Yin, Emilio Villa Cueva, Sagnik Mukherjee, Jerry Huang, Xudong Shen, Jay Gala, Hamdan Al-Ali, null Tair Djanibekov, Nurdaulet Mukhituly, Shangrui Nie, Shanya Sharma, Karolina Stanczak, Eliza Szczechla, Tiago Timponi Torrent, Deepak Tunuguntla, Marcelo Viridiano, Oskar Van Der Wal, Adina Yakefu, Aurélie Névéol, Mike Zhang, Sydney Zink, Zeerak Talat
| Challenge: | Large Language Models reproduce and exacerbate social biases present in training data, and resources to quantify this issue are limited. |
| Approach: | They propose a multilingual parallel dataset to examine culturally-specific stereotypes that may be learned by LLMs. |
| Outcome: | The proposed dataset includes stereotypes from 20 regions around the world and 16 languages, spanning multiple identity categories subject to discrimination worldwide. |