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…
Nodes Are Early, Edges Are Late: Probing Diagram Representations in Large Vision-Language Models
Haruto Yoshida, Keito Kudo, Yoichi Aoki +4
Large vision-language models (LVLMs) demonstrate strong performance on diagram understanding benchmarks, yet they still struggle with understanding relationships between elements,…
Exploring the Relationship Between Diversity and Quality in Ad Text Generation
Yoichi Aoki, Soichiro Murakami, Ukyo Honda +1
In natural language generation for advertising, creating diverse and engaging ad texts is crucial for capturing a broad audience and avoiding advertising fatigue. Regardless of the…
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…