From the 1 of 9 linked papers with an AI index.
9 papers
Value Drifts: Tracing Value Alignment During LLM Post-Training
Mehar Bhatia, Shravan Nayak, Gaurav Kamath +4
The paper studies how large language models acquire and change their alignment with human values during post‑training, analyzing the impact of supervised fine‑tuning and preference…
LACUNA: A Testbed for Evaluating Localization Precision for LLM Unlearning
Matteo Boglioni, Thibault Rousset, Siva Reddy +2
LLMs memorize sensitive training data, including personally identifiable information (PII), creating a pressing need for reliable post hoc removal methods. Unlearning has emerged a…
Toward Open Weight Models Without Risks: Separating Public and Private Capabilities in LLMs
Charbel El Feghali, Arkil Patel, Nicholas Meade +3
Open-weight Large Language Models (LLMs) enable scientific progress and broad deployment. However, they make it difficult to control access to sensitive capabilities. Current pract…
LatentLens: Revealing Highly Interpretable Visual Tokens in LLMs
Benno Krojer, Shravan Nayak, Oscar Mañas +4
Transforming a large language model (LLM) into a vision-language model (VLM) can be achieved by mapping the visual tokens from a vision encoder into the embedding space of an LLM.…
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…
Weasel: Out-of-Domain Generalization for Web Agents via Importance-Diversity Data Selection
Fatemeh Pesaran Zadeh, Seyeon Choi, Xing Han Lù +2
Large language models (LLMs) have enabled web agents that follow natural language goals through multi-step browser interactions. However, agents fine-tuned on specific trajectories…