Also at http://dirkhovy.com/
Starting with the ✨Best Paper award ✨:
"Infini-gram mini: Exact n-gram Search at the Internet Scale with FM-Index"
by Hao Xu, Jiacheng Liu, Yejin Choi, Noah A. Smith, and Hannaneh Hajishirzi
aclanthology.org/2025.emnlp-m...
1/n
Starting with the ✨Best Paper award ✨:
"Infini-gram mini: Exact n-gram Search at the Internet Scale with FM-Index"
by Hao Xu, Jiacheng Liu, Yejin Choi, Noah A. Smith, and Hannaneh Hajishirzi
aclanthology.org/2025.emnlp-m...
1/n
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
📘 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
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
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
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
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
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.
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.
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.
Generalizability of Media Frames: Corpus Creation and Analysis Across Countries
📰 We investigate how well media frames generalize across different media landscapes. The 15 MFC frames remain broadly applicable, with minor revisions of the guidelines.
Generalizability of Media Frames: Corpus Creation and Analysis Across Countries
📰 We investigate how well media frames generalize across different media landscapes. The 15 MFC frames remain broadly applicable, with minor revisions of the guidelines.
IssueBench: Millions of Realistic Prompts for Measuring Issue Bias in LLM Writing Assistance
⚖️ A foundation for measuring LLM political bias in realistic user conversations.
📍 A303 | 10:30–12:00
IssueBench: Millions of Realistic Prompts for Measuring Issue Bias in LLM Writing Assistance
⚖️ A foundation for measuring LLM political bias in realistic user conversations.
📍 A303 | 10:30–12:00
Catch our team across Main, Findings, Workshops & Demos 👇
Catch our team across Main, Findings, Workshops & Demos 👇
IssueBench, our attempt to fix this, is accepted at TACL, and I will be at #EMNLP2025 next week to talk about it!
New results 🧵
We just released IssueBench – the largest, most realistic benchmark of its kind – to answer this question more robustly than ever before.
Long 🧵with spicy results 👇
IssueBench, our attempt to fix this, is accepted at TACL, and I will be at #EMNLP2025 next week to talk about it!
New results 🧵
Not quite! In our new paper, we found that LLMs do not learn information about demographics, but instead learn individual annotators' patterns based on unique combinations of attributes!
🧵
Not quite! In our new paper, we found that LLMs do not learn information about demographics, but instead learn individual annotators' patterns based on unique combinations of attributes!
🧵
We hope SimBench can be the foundation for more specialised development of LLM simulators.
I really enjoyed working on this with @tiancheng.bsky.social et al. Many fun results 👇
The promise is revolutionary for science & policy. But there’s a huge "IF": Do these simulations actually reflect reality?
To find out, we introduce SimBench: The first large-scale benchmark for group-level social simulation. (1/9)
We hope SimBench can be the foundation for more specialised development of LLM simulators.
I really enjoyed working on this with @tiancheng.bsky.social et al. Many fun results 👇
Paper: arxiv.org/abs/2510.17516
Data: huggingface.co/datasets/pit...
Website: simbench.tiancheng.hu (9/9)
Paper: arxiv.org/abs/2510.17516
Data: huggingface.co/datasets/pit...
Website: simbench.tiancheng.hu (9/9)
It spans moral dilemmas, economic games, psych assessments & more to rigorously test how well LLMs can predict group-level human responses across a wide range of tasks. (2/9)
It spans moral dilemmas, economic games, psych assessments & more to rigorously test how well LLMs can predict group-level human responses across a wide range of tasks. (2/9)
The promise is revolutionary for science & policy. But there’s a huge "IF": Do these simulations actually reflect reality?
To find out, we introduce SimBench: The first large-scale benchmark for group-level social simulation. (1/9)
The promise is revolutionary for science & policy. But there’s a huge "IF": Do these simulations actually reflect reality?
To find out, we introduce SimBench: The first large-scale benchmark for group-level social simulation. (1/9)