5 papers
Leveraging Routing Dynamics in Mixture-of-Experts Models for Efficient Language Adaptation
Aditi Khandelwal, Marius Mosbach, Verna Dankers +2
Mixture-of-Experts (MoE) models are widely used to scale language models, yet their expert routing behavior and adaptation in a multilingual setting remain underexplored. In this w…
DeepSeek-R1 Thoughtology: Let's think about LLM Reasoning
Sara Vera MarjanoviÄ, Arkil Patel, Vaibhav Adlakha +14
Large Reasoning Models like DeepSeek-R1 mark a fundamental shift in how LLMs approach complex problems. Instead of directly producing an answer for a given input, DeepSeek-R1 creat…
Multilingual Amnesia: On the Transferability of Unlearning in Multilingual LLMs
Alireza Dehghanpour Farashah, Aditi Khandelwal, Marylou Fauchard +3
As multilingual large language models become more widely used, ensuring their safety and fairness across diverse linguistic contexts presents unique challenges. While existing rese…
The Coming Crisis of Multi-Agent Misalignment: AI Alignment Must Be a Dynamic and Social Process
Florian Carichon, Aditi Khandelwal, Marylou Fauchard +1
This position paper states that AI Alignment in Multi-Agent Systems (MAS) should be considered a dynamic and interaction-dependent process that heavily depends on the social enviro…
Cross-Lingual Multi-Hop Knowledge Editing
Aditi Khandelwal, Harman Singh, Hengrui Gu +2
Large language models are often expected to constantly adapt to new sources of knowledge and knowledge editing techniques aim to efficiently patch the outdated model knowledge, wit…