5 papers
Vector Quantized Latent Concepts: A Scalable Alternative to Clustering-Based Concept Discovery
Xuemin Yu, Ankur Garg, Samira Ebrahimi Kahou +1
Large language models (LLMs) encode rich semantic information in their hidden states, yet it remains difficult to understand what information these internal representations capture…
Cross-Layer Discrete Concept Discovery for Interpreting Language Models
Ankur Garg, Xuemin Yu, Hassan Sajjad +1
Interpreting language models remains challenging due to the existence of residual stream, which linearly mixes and duplicates features across adjacent layers, causing single-layer…
ORPR: An OR-Guided Pretrain-then-Reinforce Learning Model for Inventory Management
Lingjie Zhao, Xue Yu, Yongzhi Qi +6
As the pursuit of synergy between Artificial Intelligence (AI) and Operations Research (OR) gains momentum in handling complex inventory systems, a critical challenge persists: how…
Static Sandboxes Are Inadequate: Modeling Societal Complexity Requires Open-Ended Co-Evolution in LLM-Based Multi-Agent Simulations
Jinkun Chen, Sher Badshah, Xuemin Yu +1
What if artificial agents could not just communicate, but also evolve, adapt, and reshape their worlds in ways we cannot fully predict? With llm now powering multi-agent systems an…
Latent Concept-based Explanation of NLP Models
Xuemin Yu, Fahim Dalvi, Nadir Durrani +2
Interpreting and understanding the predictions made by deep learning models poses a formidable challenge due to their inherently opaque nature. Many previous efforts aimed at expla…