2 citations · 2 across the 2 of their papers we have counts for
10 papers
Generative Models and Connected and Automated Vehicles: A Survey in Exploring the Intersection of Transportation and AI
Bo Shu, Yiting Zhang, Saisai Hu +1
This report investigates the history and impact of Generative Models and Connected and Automated Vehicles (CAVs), two groundbreaking forces pushing progress in technology and trans…
Learning from the Right Rollouts: Data Attribution for PPO-based LLM Post-Training
Dong Shu, Denghui Zhang, Jessica Hullman
Traditional RL algorithms like Proximal Policy Optimization (PPO) typically train on the entire rollout buffer, operating under the assumption that all generated episodes provide a…
FinCall-Surprise: A Large Scale Multi-modal Benchmark for Earning Surprise Prediction
Dong Shu, Yanguang Liu, Huopu Zhang +1
Predicting corporate earnings surprises is a profitable yet challenging task, as accurate forecasts can inform significant investment decisions. However, progress in this domain ha…
Enhancing LLM Steering through Sparse Autoencoder-Based Vector Refinement
Anyi Wang, Xuansheng Wu, Dong Shu +2
Steering has emerged as a promising approach in controlling large language models (LLMs) without modifying model parameters. However, most existing steering methods rely on large-s…
Improving LLM Reasoning through Interpretable Role-Playing Steering
Anyi Wang, Dong Shu, Yifan Wang +2
Role-playing has emerged as an effective technique for enhancing the reasoning capabilities of large language models (LLMs). However, existing methods primarily rely on prompt engi…
Beyond Input Activations: Identifying Influential Latents by Gradient Sparse Autoencoders
Dong Shu, Xuansheng Wu, Haiyan Zhao +2
Sparse Autoencoders (SAEs) have recently emerged as powerful tools for interpreting and steering the internal representations of large language models (LLMs). However, conventional…