8 citations · 15 across the 9 of their papers we have counts for
10 papers
AgentCoMa: A Compositional Benchmark Mixing Commonsense and Mathematical Reasoning in Real-World Scenarios
Lisa Alazraki, Lihu Chen, Ana Brassard +3
Large Language Models (LLMs) have achieved high accuracy on complex commonsense and mathematical problems that involve the composition of multiple reasoning steps. However, current…
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…
ACORN: Aspect-wise Commonsense Reasoning Explanation Evaluation
Ana Brassard, Benjamin Heinzerling, Keito Kudo +2
Evaluating the quality of free-text explanations is a multifaceted, subjective, and labor-intensive task. Large language models (LLMs) present an appealing alternative due to their…
Chat Translation Error Detection for Assisting Cross-lingual Communications
Yunmeng Li, Jun Suzuki, Makoto Morishita +4
In this paper, we describe the development of a communication support system that detects erroneous translations to facilitate crosslingual communications due to the limitations of…
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.…
Context Limitations Make Neural Language Models More Human-Like
Tatsuki Kuribayashi, Yohei Oseki, Ana Brassard +1
Language models (LMs) have been used in cognitive modeling as well as engineering studies -- they compute information-theoretic complexity metrics that simulate humans' cognitive l…
COPA-SSE: Semi-structured Explanations for Commonsense Reasoning
Ana Brassard, Benjamin Heinzerling, Pride Kavumba +1
We present Semi-Structured Explanations for COPA (COPA-SSE), a new crowdsourced dataset of 9,747 semi-structured, English common sense explanations for Choice of Plausible Alternat…
Learning to Learn to be Right for the Right Reasons
Pride Kavumba, Benjamin Heinzerling, Ana Brassard +1
Improving model generalization on held-out data is one of the core objectives in commonsense reasoning. Recent work has shown that models trained on the dataset with superficial cu…
Diamonds in the Rough: Generating Fluent Sentences from Early-Stage Drafts for Academic Writing Assistance
Takumi Ito, Tatsuki Kuribayashi, Hayato Kobayashi +4
The writing process consists of several stages such as drafting, revising, editing, and proofreading. Studies on writing assistance, such as grammatical error correction (GEC), hav…