7 papers
Characterizing Pattern Matching and Its Limits on Compositional Task Structures
Hoyeon Chang, Jinho Park, Hanseul Cho +7
Despite impressive capabilities, LLMs' successes often rely on pattern-matching behaviors, yet these are also linked to OOD generalization failures in compositional tasks. However,…
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),…
Reasoning Models Better Express Their Confidence
Dongkeun Yoon, Seungone Kim, Sohee Yang +6
Despite their strengths, large language models (LLMs) often fail to communicate their confidence accurately, making it difficult to assess when they might be wrong and limiting the…
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…
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…
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…