1 citations · 3 across the 14 of their papers we have counts for
14 papers
Multimodal Concept Bottleneck Models
Tongqing Shi, Ge Yan, Tuomas Oikarinen +1
Concept Bottleneck Models (CBMs) enhance the interpretability of deep learning networks by aligning the features extracted from images with natural concepts. However, existing CBMs…
LLM Agents Already Know When to Call Tools -- Even Without Reasoning
Chung-En Sun, Linbo Liu, Ge Yan +2
Tool-augmented LLM agents tend to call tools indiscriminately, even when the model can answer directly. Each unnecessary call wastes API fees and latency, yet no existing benchmark…
Steer2Edit: From Activation Steering to Component-Level Editing
Chung-En Sun, Ge Yan, Zimo Wang +1
Steering methods influence Large Language Model behavior by identifying semantic directions in hidden representations, but are typically realized through inference-time activation…
Distance Marching for Generative Modeling
Zimo Wang, Ishit Mehta, Haolin Lu +5
Modern generative models based on diffusion or flow matching transport noise toward the data manifold, without representing the data manifold explicitly. We propose Distance Marchi…
Faithful and Stable Neuron Explanations for Trustworthy Mechanistic Interpretability
Ge Yan, Tuomas Oikarinen, Tsui-Wei +1
Neuron identification is a popular tool in mechanistic interpretability, aiming to uncover the human-interpretable concepts represented by individual neurons in deep networks. Whil…
ReflCtrl: Controlling LLM Reflection Efficiently via Representation Engineering
Ge Yan, Chung-En Sun, Linbo Liu +1
Large reasoning models achieve strong performance on diverse tasks by producing extended chains of thought. Self-reflection, the ability to review and revise prior reasoning steps,…
ReFIne: A Framework for Trustworthy Large Reasoning Models with Reliability, Faithfulness, and Interpretability
Chung-En Sun, Ge Yan, Akshay Kulkarni +1
Recent advances in long chain-of-thought (CoT) reasoning have largely prioritized answer accuracy and token efficiency, while overlooking aspects critical to trustworthiness. We ar…
Evaluating Neuron Explanations: A Unified Framework with Sanity Checks
Tuomas Oikarinen, Ge Yan, Tsui-Wei Weng
Understanding the function of individual units in a neural network is an important building block for mechanistic interpretability. This is often done by generating a simple text e…
Beyond Top Activations: Efficient and Reliable Crowdsourced Evaluation of Automated Interpretability
Tuomas Oikarinen, Ge Yan, Akshay Kulkarni +1
Interpreting individual neurons or directions in activation space is an important topic in mechanistic interpretability. Numerous automated interpretability methods have been propo…
Interpretable Generative Models through Post-hoc Concept Bottlenecks
Akshay Kulkarni, Ge Yan, Chung-En Sun +2
Concept bottleneck models (CBM) aim to produce inherently interpretable models that rely on human-understandable concepts for their predictions. However, existing approaches to des…
RAT: Boosting Misclassification Detection Ability without Extra Data
Ge Yan, Tsui-Wei Weng
As deep neural networks(DNN) become increasingly prevalent, particularly in high-stakes areas such as autonomous driving and healthcare, the ability to detect incorrect predictions…
ThinkEdit: Interpretable Weight Editing to Mitigate Overly Short Thinking in Reasoning Models
Chung-En Sun, Ge Yan, Tsui-Wei Weng
Recent studies have shown that Large Language Models (LLMs) augmented with chain-of-thought (CoT) reasoning demonstrate impressive problem-solving abilities. However, in this work,…
VLG-CBM: Training Concept Bottleneck Models with Vision-Language Guidance
Divyansh Srivastava, Ge Yan, Tsui-Wei Weng
Concept Bottleneck Models (CBMs) provide interpretable prediction by introducing an intermediate Concept Bottleneck Layer (CBL), which encodes human-understandable concepts to expl…
Provably Robust Conformal Prediction with Improved Efficiency
Ge Yan, Yaniv Romano, Tsui-Wei Weng
Conformal prediction is a powerful tool to generate uncertainty sets with guaranteed coverage using any predictive model, under the assumption that the training and test data are i…