2 citations · 6 across the 15 of their papers we have counts for
5 papers · 1 filter
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…
Effective Skill Unlearning through Intervention and Abstention
Yongce Li, Chung-En Sun, Tsui-Wei Weng
Large language Models (LLMs) have demonstrated remarkable skills across various domains. Understanding the mechanisms behind their abilities and implementing controls over them is…
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…
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,…