1 citations · 1 across the 5 of their papers we have counts for
5 papers
Assessing Step-by-Step Reasoning against Lexical Negation: A Case Study on Syllogism
Mengyu Ye, Tatsuki Kuribayashi, Jun Suzuki +2
Large language models (LLMs) take advantage of step-by-step reasoning instructions, e.g., chain-of-thought (CoT) prompting. Building on this, their ability to perform CoT-style rea…
Second Language Acquisition of Neural Language Models
Miyu Oba, Tatsuki Kuribayashi, Hiroki Ouchi +1
With the success of neural language models (LMs), their language acquisition has gained much attention. This work sheds light on the second language (L2) acquisition of LMs, while…
Transformer Language Models Handle Word Frequency in Prediction Head
Goro Kobayashi, Tatsuki Kuribayashi, Sho Yokoi +1
Prediction head is a crucial component of Transformer language models. Despite its direct impact on prediction, this component has often been overlooked in analyzing Transformers.…
Empirical Investigation of Neural Symbolic Reasoning Strategies
Yoichi Aoki, Keito Kudo, Tatsuki Kuribayashi +4
Neural reasoning accuracy improves when generating intermediate reasoning steps. However, the source of this improvement is yet unclear. Here, we investigate and factorize the bene…
Do Deep Neural Networks Capture Compositionality in Arithmetic Reasoning?
Keito Kudo, Yoichi Aoki, Tatsuki Kuribayashi +4
Compositionality is a pivotal property of symbolic reasoning. However, how well recent neural models capture compositionality remains underexplored in the symbolic reasoning tasks.…