67 citations · 102 across the 63 of their papers we have counts for
4 papers · 2 filters
Thought Branches: Interpreting LLM Reasoning Requires Resampling
Uzay Macar, Paul C. Bogdan, Senthooran Rajamanoharan +1
Most work interpreting reasoning models studies only a single chain-of-thought (CoT), yet these models define distributions over many possible CoTs. We argue that studying a single…
HGFM: Towards unifying Homogeneity and Heterogeneity on Text-Attributed Graphs
Trung-Kien Nguyen, Heng Ping, Shixuan Li +4
The growing interests and applications of graph learning in diverse domains have propelled the development of a unified model generalizing well across different graphs and tasks, k…
Thought Anchors: Which LLM Reasoning Steps Matter?
Paul C. Bogdan, Uzay Macar, Neel Nanda +1
Current frontier large-language models rely on reasoning to achieve state-of-the-art performance. Many existing interpretability are limited in this area, as standard methods have…
ClimateLLM: Efficient Weather Forecasting via Frequency-Aware Large Language Models
Shixuan Li, Wei Yang, Peiyu Zhang +6
Weather forecasting is crucial for public safety, disaster prevention and mitigation, agricultural production, and energy management, with global relevance. Although deep learning…