Dirk Hovy
@dirkhovy.bsky.social
Professor @milanlp.bsky.social for #NLProc, compsocsci, #ML
Also at http://dirkhovy.com/
Also at http://dirkhovy.com/
Reposted by Dirk Hovy
“Teacher Demonstrations in a BabyLM’s Zone of Proximal Development for Contingent Multi-Turn Interaction” selected for an Outstanding Paper Award at the BabyLM Challenge & Workshop!
November 24, 2025 at 10:22 AM
“Teacher Demonstrations in a BabyLM’s Zone of Proximal Development for Contingent Multi-Turn Interaction” selected for an Outstanding Paper Award at the BabyLM Challenge & Workshop!
Reposted by Dirk Hovy
See also @manoelhortaribeiro.bsky.social's post on this same topic: doomscrollingbabel.manoel.xyz/p/labeling-d...
Labeling Data with Language Models: Trick or Treat?
Large language models are now labeling data for us.
doomscrollingbabel.manoel.xyz
November 19, 2025 at 3:44 PM
See also @manoelhortaribeiro.bsky.social's post on this same topic: doomscrollingbabel.manoel.xyz/p/labeling-d...
Reposted by Dirk Hovy
Reposted by Dirk Hovy
Last week at @nlperspectives.bsky.social I presented work showing that annotators only provide the same label on ~75% of items across four NLP labelling tasks following a two week gap
November 11, 2025 at 4:44 PM
Last week at @nlperspectives.bsky.social I presented work showing that annotators only provide the same label on ~75% of items across four NLP labelling tasks following a two week gap
Reposted by Dirk Hovy
You missed one: G. Abercrombie, T. Dinkar, A. Cercas Curry, V. Rieser & @dirkhovy.bsky.social Consistency is Key: Disentangling label variation in NLP with Intra-Annotator Agreement. @nlperspectives.bsky.social
November 3, 2025 at 2:34 AM
You missed one: G. Abercrombie, T. Dinkar, A. Cercas Curry, V. Rieser & @dirkhovy.bsky.social Consistency is Key: Disentangling label variation in NLP with Intra-Annotator Agreement. @nlperspectives.bsky.social
Reposted by Dirk Hovy
🗓️ 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
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
October 31, 2025 at 2:05 PM
🗓️ 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
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
Reposted by Dirk Hovy
🗓️ 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
📘 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
October 31, 2025 at 2:05 PM
🗓️ 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
📘 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
Reposted by Dirk Hovy
🗓️ 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
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
October 31, 2025 at 2:06 PM
🗓️ 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
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
Reposted by Dirk Hovy
🗓️ 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
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
October 31, 2025 at 2:06 PM
🗓️ 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
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
Reposted by Dirk Hovy
🗓️ 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
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
October 31, 2025 at 2:06 PM
🗓️ 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
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
Reposted by Dirk Hovy
🗓️ 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
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
October 31, 2025 at 2:06 PM
🗓️ 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
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
Reposted by Dirk Hovy
🗓️ 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.
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.
October 31, 2025 at 2:06 PM
🗓️ 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.
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.
Reposted by Dirk Hovy
🗓️ 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.
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.
October 31, 2025 at 2:07 PM
🗓️ 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.
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.
Reposted by Dirk Hovy
🗓️ 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.
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.
October 31, 2025 at 2:07 PM
🗓️ 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.
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.