Masafumi Oyamada

Towards Large Language Model Organization: A Case Study on Abstractive Summarization

2023

In this work we propose ”LLM organization”, an organizational structure-based LLM workflow for improving the performance of standard abstractive summarization techniques and mitigate unfaithful summary generation. We formulated the organizational structure-based LLM workflow as a …

DISC: Dynamic Decomposition Improves LLM Inference Scaling

2025 · arXiv (Cornell University)

Inference scaling methods for LLMs often rely on decomposing problems into steps (or groups of tokens), followed by sampling and selecting the best next steps. However, these steps and their sizes are often predetermined or …