7 papers
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…
A Survey on Sparse Autoencoders: Interpreting the Internal Mechanisms of Large Language Models
Dong Shu, Xuansheng Wu, Haiyan Zhao +4
Large Language Models (LLMs) have transformed natural language processing, yet their internal mechanisms remain largely opaque. Recently, mechanistic interpretability has attracted…
Large Vision-Language Model Alignment and Misalignment: A Survey Through the Lens of Explainability
Dong Shu, Haiyan Zhao, Jingyu Hu +4
Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities in processing both visual and textual information. However, the critical challenge of alignment betwe…