BIS Reasoning 1.0: The First Large-Scale Japanese Benchmark for Belief-Inconsistent Syllogistic Reasoning
Ha-Thanh Nguyen, Chaoran Liu, Hirokazu Kiyomaru, Koichi Takeda, Yusuke Miyao, Maki Matsuda, Yusuke Oda, Pontus Stenetorp, Qianying Liu, Su Myat Noe, Hideyuki Tachibana, Kouta Nakayama, Sadao Kurohashi·June 08, 2025
Summary
BIS Reasoning 1.0 evaluates Japanese LLMs' ability to handle belief-inconsistent syllogisms, revealing significant performance discrepancies. This dataset, crucial for logical accuracy in high-stakes domains, benchmarks models like GPT-4, highlighting biases and performance gaps. It underscores the importance of improving logical consistency and objectivity in LLMs for reliable applications.
Introduction
Background
Overview of BIS Reasoning 1.0
Importance of logical reasoning in AI models
Objective
To assess the performance of Japanese LLMs in dealing with belief-inconsistent syllogisms
To highlight the significance of logical accuracy in high-stakes applications
Method
Data Collection
Description of the dataset used in BIS Reasoning 1.0
Criteria for selecting Japanese LLMs for evaluation
Data Preprocessing
Techniques employed for preparing the dataset
Handling of belief-inconsistent syllogisms in the dataset
Results
Performance Analysis
Overview of the performance of different LLMs
Identification of models with significant performance discrepancies
Bias and Performance Gaps
Analysis of biases present in the models' responses
Examination of performance gaps across various logical scenarios
Discussion
Implications for Logical Consistency
Importance of logical consistency in AI models
Challenges in achieving logical consistency in LLMs
Objectivity in AI Applications
Role of objectivity in high-stakes domains
Strategies for improving objectivity in LLMs
Conclusion
Future Directions
Recommendations for future research
Potential improvements in training and evaluation methods for LLMs
Importance of Continuous Improvement
Emphasis on the ongoing need for advancements in logical reasoning capabilities of AI models
Basic info
papers
computation and language
artificial intelligence
Advanced features
Insights
What are the key findings from benchmarking LLMs like GPT-4 using the BIS Reasoning 1.0 dataset?
Why is improving logical consistency and objectivity important for the reliable application of LLMs, as highlighted by the BIS Reasoning 1.0 dataset?
What specific types of logical fallacies or inconsistencies does BIS Reasoning 1.0 target in Japanese LLMs?
How does BIS Reasoning 1.0 evaluate the logical consistency of Japanese LLMs?