3 citations · 4 across the 15 of their papers we have counts for
12 papers · 1 filter
ContextLift: Breaking the Spurious Correlation Behind Conditional Emergent Misalignment
Florian Mai, David Kaczér
Finetuning a language model on narrowly misaligned data can make it broadly misaligned. Interleaving generic safety data is the most effective known in-training defense, but the mi…
Detecting Hidden Behaviors in LLMs via Activation-matched Finetuning
Robin Haselhorst, Lucie Flek, Florian Mai
Large language models can hide hidden behaviors that activate only under narrow conditions, such as backdoor triggers, sleeper-agent deployment cues, sandbagging, or topic-conditio…
Data Attribution of Emergent Misalignment with Persona Features
Clemens Vetter, David Kaczér, Lucie Flek +1
Emergent misalignment (EM) is the phenomenon where fine-tuning a language model on a narrow task leads to harmful behavior in unrelated domains. A leading mechanistic account attri…
A Unified Moral-Value Dataset for Instruction Tuning
Zhaohui Zeng, Florian Mai
Large language models (LLMs) have developed rapidly and become valuable tools in everyday life. However, how to align LLMs to a particular set of human values is still an open prob…
Reasoning Primitives in Hybrid and Non-Hybrid LLMs: Do Architectural Differences Yield Advantages in State-Tracking and Recall?
Shivam Rawat, Lucie Flek, Florian Mai +1
Reasoning in large language models is often discussed as a single capability, but some of its gains may stem from simpler underlying operations. We examine two such primitives, rec…
Reinforcement Learning Can Amplify Emergent Misalignment from Harmless Rewards
Magnus Jørgenvåg, David Kaczér, Lasse Ruttert +3
Emergent misalignment (EM) is the surprising tendency of language models to become broadly misaligned after fine-tuning on narrowly misaligned examples. While EM has been extensive…
Raising Bars, Not Parameters: LilMoo Compact Language Model for Hindi
Shiza Fatimah, Aniket Sen, Sophia Falk +3
The dominance of large multilingual foundation models has widened linguistic inequalities in Natural Language Processing (NLP), often leaving low-resource languages underrepresente…
Understanding Artificial Theory of Mind: Perturbed Tasks and Reasoning in Large Language Models
Christian Nickel, Laura Schrewe, Florian Mai +1
Theory of Mind (ToM) refers to an agent's ability to model the internal states of others. Contributing to the debate whether large language models (LLMs) exhibit genuine ToM capabi…
From Value Conditioning to Behavioral Shift: Lightweight Value Alignment of LLMs
Shangrui Nie, Florian Mai, David Kaczér +3
Large language models exhibit value-related preferences in their outputs, yet steering such behavior often requires large training datasets or paired preference annotations. In thi…
Judging Quality Across Languages: A Multilingual Approach to Pretraining Data Filtering with Language Models
Mehdi Ali, Manuel Brack, Max Lübbering +15
High-quality multilingual training data is essential for effectively pretraining large language models (LLMs). Yet, the availability of suitable open-source multilingual datasets r…
Learning to Plan for Language Modeling from Unlabeled Data
Nathan Cornille, Marie-Francine Moens, Florian Mai
By training to predict the next token in an unlabeled corpus, large language models learn to perform many tasks without any labeled data. However, their next-token-prediction objec…
Triple-Encoders: Representations That Fire Together, Wire Together
Justus-Jonas Erker, Florian Mai, Nils Reimers +2
Search-based dialog models typically re-encode the dialog history at every turn, incurring high cost. Curved Contrastive Learning, a representation learning method that encodes rel…