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
14
past 30d
Sources
6
distinct domains
Discusiones
0
past 30d
Latest signal
1d ago
View every signal from MilaNLP Lab →
The Milan Natural Language Processing Group #NLProc #AI milanlproc.github.io
What they're sharing

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.
Read full analysis →
View on Bluesky · ♥ 6 ↻ 3 ↩ 0 · 10 from the directory shared this · 114d ago

#TBT #NLProc 'Classist Tools: Social Class Correlates with Performance in NLP' by Curry et al. (2024) finds NLP tools perform worse on lower-SES speech - higher perplexity, more ASR errors, dialect flagged as "wrong" by grammar checkers. arxiv.org/pdf/2403.04445

arxiv.org
View on Bluesky · ♥ 3 ↻ 2 ↩ 0 · 3 from the directory shared this · 9d ago

#MemoryModay #NLProc Countering Hateful and Offensive Speech Online - Open Challenges" (Flor Miriam Plaza-Del-Arco, @deboranozza.bsky.social, et al. 2024) presented a tutorial on the challenges and solutions for detecting and mitigating hate speech online. aclanthology.org/202…

Countering Hateful and Offensive Speech Online - Open Challenges aclanthology.org
AI Weekly's analysis →
  • The EMNLP 2024 tutorial in Miami, by five researchers, treats online hate-speech moderation as a set of open challenges, not a solved product.
  • Topics include multilingualism, counter-narrative generation, a hands-on session with a popular hate-speech detection API, and fairness and ethics in AI.
  • The abstract names no specific API, no detector numbers, and frames safer online spaces as a collaboration goal rather than a shipped technology.
Read full analysis →
View on Bluesky · ♥ 4 ↻ 3 ↩ 0 · 2 from the directory shared this · 12d 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.
Read full analysis →
View on Bluesky · ♥ 11 ↻ 5 ↩ 0 · 2 from the directory shared this · 93d ago

#TBT #NLProc 'Understanding Political Discourse on Twitter through Election Manifestos' Maurer et al. (2024) present a method for predicting party positioning from tweets aclanthology.org/2024.finding...

Toeing the Party Line: Election Manifestos as a Key to Understand Political Discourse on Twitter aclanthology.org
AI Weekly's analysis →
  • An EMNLP 2024 paper extends manifesto-based party-position modeling to Twitter, using hashtags as a fine-tuning signal in place of hand-labeled data.
  • The authors report that the resulting party positionings are stable and consistent with those derived from official manifestos.
  • The method holds up in low-resource settings, working when only smaller subsets from shorter time periods are available.
Read full analysis →
View on Bluesky · ♥ 3 ↻ 2 ↩ 0 · 2 from the directory shared this · 1d ago

#MemoryModay #NLProc 'SocioProbe: What, When, and Where Language Models Learn about Sociodemographics' - Lauscher et al. delve into language models' grasp of sociodemographics. Their findings? Models learn, but don't apply it. aclanthology.org/2022.emnlp-m...

SocioProbe: What, When, and Where Language Models Learn about Sociodemographics aclanthology.org
AI Weekly's analysis →
  • SocioProbe probes single-GPU PLMs on multiple English data sets using classifier probing and information-theoretic minimum description length probing.
  • The authors find that PLMs do encode gender and age, and that this knowledge is sometimes spread across the layers of tested models.
  • Models that excel in general language understanding do not seem to own more sociodemographic knowledge, which requires large amounts of pre-training data.
Read full analysis →
View on Bluesky · ♥ 6 ↻ 2 ↩ 0 · 2 from the directory shared this · 26d ago

We're back with the reading group! Today Lorena Calvo-Bartolomé presented "The Alternative Annotator Test for LLM-as-a-Judge: How to Statistically Justify Replacing Human Annotators with LLMs" Paper: aclanthology.org/2025.acl-lon... #NLProc #LLMasajudge

aclanthology.org
View on Bluesky · ♥ 7 ↻ 3 ↩ 0 · 2 from the directory shared this · 37d ago

#MemoryModay #NLProc Outstanding Paper at ACL 2024! @paul-rottger.bsky.social et al. evaluate LLM values and opinions in 'Political Compass or Spinning Arrow?' aclanthology.org/2024.acl-lon...

Political Compass or Spinning Arrow? Towards More Meaningful Evaluations for Values and Opinions in Large Language Models aclanthology.org
AI Weekly's analysis →
  • An ACL 2024 Outstanding Paper argues that LLM political-bias tests using multiple-choice surveys do not reflect how real users query models.
  • In a Political Compass Test case study, models gave substantively different answers when not forced into the test's fixed-choice format.
  • The authors also report that LLM answers shift depending on how models are constrained, and lack paraphrase robustness.
Read full analysis →
View on Bluesky · ♥ 8 ↻ 2 ↩ 0 · 2 from the directory shared this · 40d 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.
Read full analysis →
View on Bluesky · ♥ 6 ↻ 2 ↩ 0 · 2 from the directory shared this · 82d 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.
Read full analysis →
View on Bluesky · ♥ 4 ↻ 3 ↩ 0 · 2 from the directory shared this · 96d 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.
Read full analysis →
View on Bluesky · ♥ 6 ↻ 1 ↩ 1 · 2 from the directory shared this · 107d 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 · 127d 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 · 136d ago

👋 We’re delighted to welcome @celianri.bsky.social, who’ll be visiting our lab over the next few months! Her research explores moderation practices on Reddit, combining social-graph signals with NLP to predict moderation outcomes. Great to have you with us, Célia!

View on Bluesky · ♥ 12 ↻ 3 ↩ 0 · 26d 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 · 96d 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 · 138d 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 · 116d ago

For last week's @milanlp.bsky.social seminar, we had the pleasure to host Hongyu Chen. She presented her upcoming EMNLP paper "Contrast Comes at a Cost: Prompting Large Language Models for Perception-Centered Gendered Style Transfer". #NLProc

View on Bluesky · ♥ 8 ↻ 2 ↩ 0 · 18d 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 · 129d 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 · 79d ago
Their network

In MilaNLP Lab's orbit

Center = MilaNLP Lab. Left = members they follow (green edges). Right = members who follow them (blue edges). Top = mutual follows (orange edges, slightly larger). Drag any node to reposition; click to open that profile.

Are you MilaNLP Lab? Show it.

Add the Who’s Who of AI badge to your site or bio. It links back to this profile.

Listed in AI Weekly's Who's Who of AI

Markdown: [![Listed in AI Weekly's Who's Who of AI](https://aiweekly.co/modules/custom/aiweekly_whoswho/images/whoswho-badge.svg)](https://aiweekly.co/whos-who/person/milanlp-bsky-social)