6 papers
Scalable Circuit Learning for Interpreting Large Language Models
Naiyu Yin, Dennis Wei, Tian Gao +3
A prominent research direction in mechanistic interpretability is learning sparse circuits over LLM components to reveal how they jointly produce model behavior. However, raw neuro…
CoFrGeNet: Continued Fraction Architectures for Language Generation
Amit Dhurandhar, Vijil Chenthamarakshan, Dennis Wei +3
Transformers are arguably the preferred architecture for language generation. In this paper, inspired by continued fractions, we introduce a new function class for generative model…
ICX360: In-Context eXplainability 360 Toolkit
Dennis Wei, Ronny Luss, Xiaomeng Hu +6
Large Language Models (LLMs) have become ubiquitous in everyday life and are entering higher-stakes applications ranging from summarizing meeting transcripts to answering doctors'…
Multi-Level Explanations for Generative Language Models
Lucas Monteiro Paes, Dennis Wei, Hyo Jin Do +8
Despite the increasing use of large language models (LLMs) for context-grounded tasks like summarization and question-answering, understanding what makes an LLM produce a certain r…
Agentic AI Needs a Systems Theory
Erik Miehling, Karthikeyan Natesan Ramamurthy, Kush R. Varshney +11
The endowment of AI with reasoning capabilities and some degree of agency is widely viewed as a path toward more capable and generalizable systems. Our position is that the current…
Identifying Sub-networks in Neural Networks via Functionally Similar Representations
Tian Gao, Amit Dhurandhar, Karthikeyan Natesan Ramamurthy +1
Providing human-understandable insights into the inner workings of neural networks is an important step toward achieving more explainable and trustworthy AI. Existing approaches to…