13 citations · 36 across the 27 of their papers we have counts for
12 papers · 1 filter
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…
AutoSCORE: Enhancing Automated Scoring with Multi-Agent Large Language Models via Structured Component Recognition
Yun Wang, Zhaojun Ding, Xuansheng Wu +3
Automated scoring plays a crucial role in education by reducing the reliance on human raters, offering scalable and immediate evaluation of student work. While large language model…
LMOD+: A Comprehensive Multimodal Dataset and Benchmark for Developing and Evaluating Multimodal Large Language Models in Ophthalmology
Zhenyue Qin, Yang Liu, Yu Yin +13
Vision-threatening eye diseases pose a major global health burden, with timely diagnosis limited by workforce shortages and restricted access to specialized care. While multimodal…
Common Inpainted Objects In-N-Out of Context
Tianze Yang, Tyson Jordan, Ruitong Sun +2
We present Common Inpainted Objects In-N-Out of Context (COinCO), a novel dataset addressing the scarcity of out-of-context examples in existing vision datasets. By systematically…
Concept-Centric Token Interpretation for Vector-Quantized Generative Models
Tianze Yang, Yucheng Shi, Mengnan Du +4
Vector-Quantized Generative Models (VQGMs) have emerged as powerful tools for image generation. However, the key component of VQGMs -- the codebook of discrete tokens -- is still n…
Artificial Intelligence Bias on English Language Learners in Automatic Scoring
Shuchen Guo, Yun Wang, Jichao Yu +7
This study investigated potential scoring biases and disparities toward English Language Learners (ELLs) when using automatic scoring systems for middle school students' written re…
Denoising Concept Vectors with Sparse Autoencoders for Improved Language Model Steering
Haiyan Zhao, Xuansheng Wu, Fan Yang +3
Linear concept vectors effectively steer LLMs, but existing methods suffer from noisy features in diverse datasets that undermine steering robustness. We propose Sparse Autoencoder…
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…
Towards Trustworthy GUI Agents: A Survey
Yucheng Shi, Wenhao Yu, Jingyuan Huang +3
Graphical User Interface (GUI) agents extend large language models from text generation to action execution in real-world digital environments. Unlike conversational systems, GUI a…
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…
Interpreting and Steering LLMs with Mutual Information-based Explanations on Sparse Autoencoders
Xuansheng Wu, Jiayi Yuan, Wenlin Yao +2
Large language models (LLMs) excel at handling human queries, but they can occasionally generate flawed or unexpected responses. Understanding their internal states is crucial for…
Self-Regularization with Sparse Autoencoders for Controllable LLM-based Classification
Xuansheng Wu, Wenhao Yu, Xiaoming Zhai +1
Modern text classification methods heavily rely on contextual embeddings from large language models (LLMs). Compared to human-engineered features, these embeddings provide automati…