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Debora Nozza

79 trust researcher @deboranozza.bsky.social · 451 followers
AI research NLP & language Responsible AI
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Researcher with public evidence across AI research, NLP & language, Responsible AI.

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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
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  • 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

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
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  • 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

#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
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  • 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

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

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

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

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

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

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

#MemoryModay #NLProc 'Two Contrasting Data Annotation Paradigms for Subjective NLP Tasks' by @paul-rottger.bsky.social et al. (2022) explores descriptive and prescriptive data labeling paradigms. aclanthology.org/2022.naacl-m...

Two Contrasting Data Annotation Paradigms for Subjective NLP Tasks aclanthology.org
AI Weekly's analysis →
  • Röttger et al. define two contrasting annotation paradigms: descriptive (encourages annotator subjectivity) and prescriptive (discourages it).
  • Descriptive annotation supports surveying and modelling different beliefs; prescriptive annotation trains models to apply one belief consistently.
  • The NAACL 2022 paper illustrates the contrast with a hate-speech annotation experiment and urges creators to pick one paradigm.
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↻ Debora Nozza reposted
MilaNLP Lab @milanlp.bsky.social

#TBT #NLProc 'Data-Efficient Strategies for Expanding Hate Speech Detection into Under-Resourced Languages' funnels limited target-language data to fine-tune models & enhance effectiveness. By @paul-rottger.bsky.social et al. aclanthology.org/2022.emnlp-m...

Data-Efficient Strategies for Expanding Hate Speech Detection into Under-Resourced Languages aclanthology.org
AI Weekly's analysis →
  • Röttger and co-authors find a small amount of target-language fine-tuning data is enough to achieve strong hate speech detection performance.
  • The benefits of adding more target-language annotation decrease exponentially, the EMNLP 2022 paper reports across five non-English languages.
  • Initial fine-tuning on English data can partially substitute for target-language labels and improve model generalisability, per the study.
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