5 accepted papers
Language models obtain extensive capabilities through pre-training. However, the pre-training dynamics remains a black box. In this work, we track linear interpretable feature evolution across pre-training snapshots using a sparse dictionary learning method called crosscoders. We find that most feat…
For optical cooperative localization, which employs optical beacons with prior features as cooperative targets, a fundamental prerequisite is to ensure that the beacons are always captured by the vision sensors during the entire localization process. In other words, there is an applicability issue o
Techniques enabling large language models (LLMs) to "think more" by generating and attending to intermediate reasoning steps have shown promise in solving complex problems. However, the standard approaches generate sequences of discrete tokens immediately before responding, and so they can incur sig…
Effective conversational agents must personalize their interactions to adapt to user preferences, personalities, and attributes across diverse domains like education and healthcare. Current methods like Reinforcement Learning from Human Feedback (RLHF), often prioritize helpfulness and safety but fa…
LLM-powered personalization agent systems employ Large Language Models (LLMs) to predict users’ behavior from their past activities. However, their effectiveness often hinges on the ability to effectively leverage extensive, long user historical data due to its inherent noise and length of such data…