Who's Who of AI

MilaNLP Lab

78 trust researcher @milanlp.bsky.social · 498 followers
NLP & language AI research
Why they matter

Researcher with public evidence across NLP & language, AI research.

AI signals
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past 30d
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past 30d
Latest signal
19d ago
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The Milan Natural Language Processing Group #NLProc #AI milanlproc.github.io
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Articles & links

For today's reading group, @marlutz.bsky.social presented "State media control influences large language models" by Waight et al. (2026) Paper: www.nature.com/articles/s41... #NLProc

State media control influences large language models | Nature nature.com
AI Weekly's analysis →
  • Chinese state-media content appears in typical LLM training sets at roughly 41 times the rate of Chinese-language Wikipedia.
  • Across 37 countries, models prompted in the local language produce more regime-favorable responses in countries with lower press freedom.
  • A pretraining experiment with just 6,400 state-scripted documents pushed an open-weight model to pro-government responses nearly 80 percent of the time.
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View on Bluesky · ♥ 6 ↻ 3 ↩ 0 · 8 from the directory shared this · 54d ago

For today's reading group, @veraneplenbroek.bsky.social presented "Old Habits Die Hard: How Conversational History Geometrically Traps LLMs" by Simhi et al. (2026) Paper: arxiv.org/abs/2603.03308 #NLProc

[2603.03308] Old Habits Die Hard: How Conversational History Geometrically Traps LLMs arxiv.org
AI Weekly's analysis →
  • A new arXiv paper introduces History-Echoes, a framework showing conversation history biases what large language models generate next.
  • Across three model families and six datasets, the authors find gaps in latent space form a 'geometric trap' confining a model's response trajectory.
  • The work suggests hallucinations from earlier turns can influence later responses, implying an early mistake tends to persist inside a session.
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View on Bluesky · ♥ 11 ↻ 5 ↩ 0 · 2 from the directory shared this · 33d ago

#MemoryModay #NLProc 'My Answer is C' by Wang et al. (2024) underscores the scrutiny needed for full text responses in LLMs multi-choice evaluations. aclanthology.org/2024.finding...

“My Answer is C”: First-Token Probabilities Do Not Match Text Answers in Instruction-Tuned Language Models aclanthology.org
AI Weekly's analysis →
  • A Findings of ACL 2024 paper reports mismatch rates over 60% between first-token log-probability rankings and the model's actual text answer.
  • The gap holds across final option choice, refusal rate, choice distribution and robustness under prompt perturbation, according to the authors.
  • Models heavily fine-tuned on conversational or safety data are especially impacted, and constraining prompts to force an option letter does not close the gap.
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View on Bluesky · ♥ 6 ↻ 2 ↩ 0 · 2 from the directory shared this · 22d ago

#MemoryModay #NLProc Fornaciari et al.'s 2022 'Hard and Soft Evaluation of NLP models with BOOtSTrap SAmpling - BooStSa' is a useful Python tool for benchmarking NLP predictions with bootstrapped sampling. aclanthology.org/2022.acl-dem...

Hard and Soft Evaluation of NLP models with BOOtSTrap SAmpling - BooStSa aclanthology.org
AI Weekly's analysis →
  • BooStSa is an ACL 2022 demo tool that automates bootstrap-sampling significance tests for comparing NLP model results across many conditions.
  • Unlike prior helpers, it handles both standard categorical predictions and soft-label outputs expressed as probability distributions across classes.
  • Tommaso Fornaciari, Alexandra Uma, Massimo Poesio and Dirk Hovy presented the tool at ACL 2022 in Dublin, Ireland.
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View on Bluesky · ♥ 4 ↻ 3 ↩ 0 · 2 from the directory shared this · 36d ago

For today's reading group, @esradonmez.bsky.social presented "RLHF May Not Reflect Genuine Preferences" by Ghafouri et al. (2026). Interesting thoughts on whether annotations are actually real preferences! Paper: arxiv.org/abs/2604.03238 #NLProc #RLHF

RLHF May Not Reflect Genuine Preferences arxiv.org
AI Weekly's analysis →
  • A new arxiv preprint argues RLHF annotator responses may not represent genuine preferences at all, but responses constructed on the spot.
  • Filtering high-inconsistency annotators in two RLHF datasets flipped majority harm classifications for 18.6% of prompts.
  • The same filtering shifted mean ratings by more than 13 points on a 100-point scale, suggesting systematic rather than random noise.
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View on Bluesky · ♥ 6 ↻ 1 ↩ 1 · 2 from the directory shared this · 47d ago

#TBT #NLProc Hessenthaler et al.'s 2022 work delves into AI's link with fairness & energy reduction in English NLP models, challenging bias reduction theories. #AI #NLP #sustainability aclanthology.org/2022.emnlp-m...

Bridging Fairness and Environmental Sustainability in Natural Language Processing aclanthology.org
AI Weekly's analysis →
  • An EMNLP 2022 paper reports that knowledge distillation, a common efficiency technique, can actually decrease model fairness rather than preserve it.
  • The case study evaluates distilled models on natural language inference and semantic similarity, with gender bias measured via the Word Embedding Association Test.
  • The authors argue fairness and environmental sustainability are studied in isolation, and that an exclusive focus on one can hinder the other.
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View on Bluesky · ♥ 7 ↻ 1 ↩ 0 · 2 from the directory shared this · 47d ago

For today's reading group, @pia-p.bsky.social presented "When Personalization Meets Reality: A Multi-Faceted Analysis of Personalized Preference Learning" by Yijiang River Dong et al. (2025) Paper: aclanthology.org/2025.finding... #NLProc

aclanthology.org
View on Bluesky · ♥ 8 ↻ 2 ↩ 0 · 2 from the directory shared this · 75d ago

#TBT #NLProc "Narratives at Conflict" by Sinelnik and @dirkhovy.bsky.social looks at hidden tactics of disinformation campaigns! They analyzed 8,000 news articles across 4 languages to reveal how disinformation campaigns adapt narratives for different audiences. 🕵️‍♀️ aclantho…

Narratives at Conflict: Computational Analysis of News Framing in Multilingual Disinformation Campaigns aclanthology.org
AI Weekly's analysis →
  • Sinelnik and Hovy analyze roughly 8,000 news articles from eight years of Russia-backed disinformation campaigns spanning four languages and fifteen target countries.
  • The paper finds campaigns consistently favor specific frames depending on the audience's language, and Russian-language coverage varies frames by target region.
  • The two most prominent automatic frame-analysis models underperform and show high disagreement, exposing a gap in multilingual detection tooling.
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View on Bluesky · ♥ 5 ↻ 2 ↩ 0 · 2 from the directory shared this · 19d ago

#TBT #NLProc 'Language is Scary when Over-Analyzed...' by @arimuti.bsky.social et al. explores argumentative reasoning in misogyny detection (2024). Detecting implicit misogyny proves challenging for language models. aclanthology.org/2024.emnlp-m...

Language is Scary when Over-Analyzed: Unpacking Implied Misogynistic Reasoning with Argumentation Theory-Driven Prompts aclanthology.org
AI Weekly's analysis →
  • The paper reframes misogyny detection as an argumentative reasoning task and tests whether LLMs can supply the missing link to implied meaning.
  • Authors conclude LLMs 'fall short on reasoning capabilities' about misogynistic comments and default to internalized stereotypes about women.
  • The study covers Italian and English, using zero-shot and few-shot prompts with chain-of-thought reasoning and augmented knowledge techniques.
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View on Bluesky · ♥ 3 ↻ 2 ↩ 0 · 2 from the directory shared this · 26d ago

#TBT #NLProc Bergman et al.'s 'Guiding the Release of Safer E2E Conversational AI through Value Sensitive Design' explores AI launch with a value-sensitive lens. aclanthology.org/2022.sigdial...

Guiding the Release of Safer E2E Conversational AI through Value Sensitive Design aclanthology.org
AI Weekly's analysis →
  • A 2022 SIGDIAL paper proposes a framework for deciding whether and how to release end-to-end conversational AI, grounded in value-sensitive design.
  • The authors argue such models trained on internet data may learn toxic or otherwise harmful language, forcing tradeoffs between positive impact and harm.
  • The contribution is not a mitigation technique but decision guidance for practitioners, surveying tensions between values, potential positive impact, and potential harms.
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View on Bluesky · ♥ 4 ↻ 2 ↩ 0 · 2 from the directory shared this · 40d ago

#MemoryMonday #NLProc 'Entropy-based Attention Regularization Frees Unintended Bias Mitigation from Lists' by Attanasio et al. redefines bias reduction in #AI, sans prior term knowledge. #2022Publication aclanthology.org/2022.finding...

Entropy-based Attention Regularization Frees Unintended Bias Mitigation from Lists aclanthology.org
AI Weekly's analysis →
  • EAR adds an objective that penalizes tokens with low self-attention entropy, discouraging BERT from overfitting to specific training terms.
  • Across three benchmark corpora in English and Italian, EAR matches or exceeds state-of-the-art for hate-speech classification and bias metrics.
  • Because the method needs no list, it also surfaces the terms most likely to induce bias as a diagnostic byproduct.
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View on Bluesky · ♥ 5 ↻ 2 ↩ 0 · 2 from the directory shared this · 43d ago
Their own posts

Recent commentary

@taniseceron.bsky.social is presenting her work about political content in pre-training and post-training data at the AI & Society conference. #AIandSociety #NLProc

View on Bluesky · ♥ 17 ↻ 5 ↩ 0 · 67d ago

🎉 Excited to welcome Lorena Calvo Bartolomé to our lab! Her expertise in NLP and signal processing will bring fresh perspectives to our research. She will be working on TOLD, a project exploring voice-based data collection as a richer alternative to written annotation. #NLProc

View on Bluesky · ♥ 11 ↻ 5 ↩ 0 · 76d ago

🧠🤖 It was a pleasure to host @andreadevarda.bsky.social for his talk, "Large Language Models as Models of Human Language(s) and Higher-Level Cognition." A truly inspiring talk! #NLProc

View on Bluesky · ♥ 9 ↻ 3 ↩ 0 · 36d ago

We had the pleasure of hosting @tresiwald.bsky.social and Alireza Salemi at our latest seminar. Andreas spoke about the reliability of language models through computational argumentation, while Alireza presented his work on personalizing LLMs. Thank you both for the inspiring talks! #NLProc

View on Bluesky · ♥ 9 ↻ 3 ↩ 0 · 77d ago

We were honored to welcome Giulia Barbareschi to our lab for an inspiring conversation on inclusive technology and what NLP should learn from it. Thank you for sharing your insights and vision! #InclusiveTech #Accessibility #NLProc

View on Bluesky · ♥ 7 ↻ 3 ↩ 0 · 56d ago

Last Friday, we had the pleasure of hosting Clement Jonathan Mazet-Sonilhac and Monika Kaczorowska for an insightful discussion on AI adoption and its impact on the quality of financial services. #NLProc #AI

View on Bluesky · ♥ 6 ↻ 1 ↩ 0 · 69d ago

It was a pleasure to host @radamihalcea.bsky.social at our weekly lab seminar. Thank you for the inspiring talk and thought-provoking discussion on the importance of the long tail in NLP. We really enjoyed the conversation!

View on Bluesky · ♥ 5 ↻ 1 ↩ 0 · 19d ago
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