4 papers
LLMs Faithfully and Iteratively Compute Answers During CoT: A Systematic Analysis With Multi-step Arithmetics
Keito Kudo, Yoichi Aoki, Tatsuki Kuribayashi +5
This study investigates the internal information flow of large language models (LLMs) while performing chain-of-thought (CoT) style reasoning. Specifically, with a particular inter…
First Heuristic Then Rational: Dynamic Use of Heuristics in Language Model Reasoning
Yoichi Aoki, Keito Kudo, Tatsuki Kuribayashi +4
Multi-step reasoning instruction, such as chain-of-thought prompting, is widely adopted to explore better language models (LMs) performance. We report on the systematic strategy th…
TNF: Tri-branch Neural Fusion for Multimodal Medical Data Classification
Tong Zheng, Shusaku Sone, Yoshitaka Ushiku +2
This paper presents a Tri-branch Neural Fusion (TNF) approach designed for classifying multimodal medical images and tabular data. It also introduces two solutions to address the c…
WeaveNet for Approximating Two-sided Matching Problems
Shusaku Sone, Jiaxin Ma, Atsushi Hashimoto +2
Matching, a task to optimally assign limited resources under constraints, is a fundamental technology for society. The task potentially has various objectives, conditions, and cons…