Who's Who of AI

Debora Nozza

79 trust researcher @deboranozza.bsky.social · 451 followers
AI research NLP & language Responsible AI
Why they matter

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

AI signals
14
past 30d
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distinct domains
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past 30d
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6d ago
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Assistant Professor at Bocconi University in MilaNLP group • Working in #NLP, #CSS and #Ethics • She/her • #ERCStG PERSONAE
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Articles & links

↻ Debora Nozza reposted
MilaNLP Lab @milanlp.bsky.social

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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↻ Debora Nozza reposted
MilaNLP Lab @milanlp.bsky.social

#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...

arxiv.org View on Bluesky →
↻ Debora Nozza reposted
MilaNLP Lab @milanlp.bsky.social

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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↻ Debora Nozza reposted
MilaNLP Lab @milanlp.bsky.social

#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.
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↻ Debora Nozza reposted
MilaNLP Lab @milanlp.bsky.social

For this week reading group, @arimuti.bsky.social presented "Ownership in AI-Assisted Everyday Tasks" by Megan Wei et al. (2026) Paper: arxiv.org/abs/2609.20658 #NLProc

Ownership in AI-Assisted Everyday Tasks arxiv.org
AI Weekly's analysis →
  • In a qualitative survey, 42% of respondents said they could 'definitely' have done high-ownership tasks without AI, versus 63% for low-ownership tasks.
  • Researchers at Brown, Carnegie Mellon, Berkeley and the Artificiality Institute surveyed 64 participants, with 52 complete responses, on two recent AI-assisted tasks each.
  • Loss of personal voice and lack of comprehension both erode felt ownership; willingness to disclose AI use is decoupled from pride and shaped by community norms.
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↻ Debora Nozza reposted
MilaNLP Lab @milanlp.bsky.social

#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.
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↻ Debora Nozza reposted
MilaNLP Lab @milanlp.bsky.social

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 →
↻ Debora Nozza reposted
MilaNLP Lab @milanlp.bsky.social

#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.
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↻ Debora Nozza reposted
MilaNLP Lab @milanlp.bsky.social

#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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