6 papers
Contrastive Weak-to-strong Generalization
Houcheng Jiang, Junfeng Fang, Jiaxin Wu +5
Weak-to-strong generalization provides a promising paradigm for scaling large language models (LLMs) by training stronger models on samples from aligned weaker ones, without requir…
Gemini Embedding 2: A Native Multimodal Embedding Model from Gemini
Madhuri Shanbhogue, Zhe Li, Shanfeng Zhang +86
We introduce Gemini Embedding 2, a native multimodal embedding model that allows embedding video, audio, image, and text modalities in a unified representation space. We leverage t…
Evolution of Concepts in Language Model Pre-Training
Xuyang Ge, Wentao Shu, Jiaxing Wu +3
Language models obtain extensive capabilities through pre-training. However, the pre-training process remains a black box. In this work, we track linear interpretable feature evolu…
Enhancing Personalized Multi-Turn Dialogue with Curiosity Reward
Yanming Wan, Jiaxing Wu, Marwa Abdulhai +2
Effective conversational agents like large language models (LLMs) must personalize their interactions to adapt to user preferences, personalities, and attributes across diverse dom…
RLPF: Reinforcement Learning from Prediction Feedback for User Summarization with LLMs
Jiaxing Wu, Lin Ning, Luyang Liu +7
LLM-powered personalization agent systems employ Large Language Models (LLMs) to predict users' behavior from their past activities. However, their effectiveness often hinges on th…
Deliberation in Latent Space via Differentiable Cache Augmentation
Luyang Liu, Jonas Pfeiffer, Jiaxing Wu +2
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…