5 papers · 1 filter
TSLM: Tree-Structured Language Modeling for Divergent Thinking
Doyoung Kim, Jaehyeok Doo, Minjoon Seo
Language models generate reasoning sequentially, preventing them from decoupling irrelevant exploration paths during search. We introduce Tree-Structured Language Modeling (TSLM),…
The CoT Encyclopedia: Analyzing, Predicting, and Controlling how a Reasoning Model will Think
Seongyun Lee, Seungone Kim, Minju Seo +9
Long chain-of-thought (CoT) is an essential ingredient in effective usage of modern large language models, but our understanding of the reasoning strategies underlying these capabi…
Latent Reasoning via Sentence Embedding Prediction
Hyeonbin Hwang, Byeongguk Jeon, Seungone Kim +7
Autoregressive language models (LMs) generate one token at a time, yet human reasoning operates over higher-level abstractions - sentences, propositions, and concepts. This contras…
How language models extrapolate outside the training data: A case study in Textualized Gridworld
Doyoung Kim, Jongwon Lee, Jinho Park +1
Language models' ability to extrapolate learned behaviors to novel, more complex environments beyond their training scope is highly unknown. This study introduces a path planning t…
How Do Large Language Models Acquire Factual Knowledge During Pretraining?
Hoyeon Chang, Jinho Park, Seonghyeon Ye +4
Despite the recent observation that large language models (LLMs) can store substantial factual knowledge, there is a limited understanding of the mechanisms of how they acquire fac…