#TBT #NLProc "Explaining Speech Classification Models" by Pastor et al. (2024) makes speech classification more transparent! 🔍 Their research reveals which words matter most and how tone and background noise impact decisions.

#MemoryModay #NLProc 'Measuring Harmful Representations in Scandinavian Language Models' uncovers gender bias, challenging Scandinavia's equity image.

For our weekly lab seminar it was a pleasure to have @ValerioCapraro talking about The Economics of Language. #NLProc

🗓️ Nov 6 – Oral Presentation (TACL) IssueBench: Millions of Realistic Prompts for Measuring Issue Bias in LLM Writing Assistance ⚖️ A foundation for measuring LLM political bias in realistic user conversations. 📍 A303 | 10:30–12:00

🗓️ Nov 8 – STARSEM Workshop Generalizability of Media Frames: Corpus Creation and Analysis Across Countries 📰 We investigate how well media frames generalize across different media landscapes. The 15 MFC frames remain broadly applicable, with minor revisions of the guidelines.

🗓️ Nov 8 – BabyLM Workshop Teacher Demonstrations in a BabyLM's Zone of Proximal Development for Contingent Multi-Turn Interaction 👶 ContingentChat, a Teacher–Student framework that benchmarks and improves multi-turn contingency in a BabyLM trained on 100M words.

🗓️ Nov 8 – NLPerspectives Workshops Balancing Quality and Variation 🧮 For datasets to represent diverse opinions, they must preserve variation while filtering out spam. We evaluate annotator filtering heuristics and show how they often remove genuine variation.

🗓️ Nov 8 – WiNLP Workshops No for Some, Yes for Others 🤖 We investigate how sociodemographic persona prompts affect false refusal behaviors in LLMs. Model and task type are the dominant factors driving these refusals.

🗓️ Nov 7 – Main Conference Posters TrojanStego: Your Language Model Can Secretly Be a Steganographic Privacy-Leaking Agent 🔒 LLMs can be fine-tuned to leak secrets via token-based steganography! 📍 Hall C | 10:30–12:00

🗓️ Nov 7 – Main Conference Posters Principled Personas: Defining and Measuring the Intended Effects of Persona Prompting on Task Performance 🧍 Discussing different applications for LLM persona prompting, and how to measure their success. 📍 Hall C | 10:30–12:00

🗓️ Nov 6 – Findings Posters The “r” in “woman” stands for rights. 💬 We propose a taxonomy of social dynamics in implicit misogyny (EN,IT), auditing 9 LLMs — and they consistently fail. The more social knowledge a message requires, the worse they perform. 📍 Hall C | 12:30–13:30

🗓️ Nov 5 - Demo Co-DETECT: Collaborative Discovery of Edge Cases in Text Classification 🧩 Co-DETECT – an iterative, human-LLM collaboration framework for surfacing edge cases and refining annotation codebooks in text classification. 📍 Demo Session 2 – Hall C3 | 14:30–16:00

🗓️ Nov 5 – Main Conference Posters 📘 Biased Tales A dataset of 5k short LLM bedtime stories generated across sociocultural axes with an evaluation taxonomy for character-centric attributes and context-centric attributes. 📍 Hall C | 11:00–12:30

🗓️ Nov 5 – Main Conference Posters Personalization up to a Point 🧠 In the context of content moderation, we show that fully personalized models can perpetuate hate speech, and propose a policy-based method to impose legal boundaries. 📍 Hall C | 11:00–12:30

Proud to present our #EMNLP2025 papers! Catch our team across Main, Findings, Workshops & Demos 👇

#TBT #NLProc Explore 'Wisdom of Instruction-Tuned LLM Crowds' by Plaza et al. LLM labels outperform single models in tasks & languages. But few-shot can't top zero-shot. Supervised models rule.

Great session today in our lab reading group. Thanks to @EmaLearns for presenting the article “Universities are embracing AI: will students get smarter or stop thinking?” from @naturemagazine.bsky.social. Article: https://t.co/O1Cr7Pa93T #NLProc

For our last Thursday Reading Group, @taniseceron presented "Helpful, harmless, honest? Sociotechnical limits of AI alignment and safety through Reinforcement Learning from Human Feedback" by A. D. Lindström et al. (2025) Paper: https://t.co/V785CHGUKC #NLProc

#MemoryModay #NLProc 'Universal Joy: A Data Set and Results for Classifying Emotions Across Languages' by Lamprinidis et al. (2021) explores how AI research affects our planet.

#TBT #NLProc 'Classist Tools: Social Class Correlates with Performance in NLP' by Curry et al. (2024) explores AI's hidden energy problem, and how machine learning impacts environmental sustainability.