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cloud.google.com·6h ago
AI Research
arXiv CS.AI·Aug 3·5 min read
Benchmarking LLM Competence on Logical Inference over Probability Operators
TL;DR
A new benchmark for evaluating large language models (LLMs) on logical inference involving probability operators was developed, revealing significant biases in model responses. Only 9 out of 29 models performed better than random chance, indicating a competence floor in their reasoning abilities.
✦ Why It Matters
Evaluate your LLMs against this benchmark to identify and mitigate biases in logical reasoning.
Key Takeaways
- Implement the new benchmark to assess your LLMs' reasoning capabilities.
- Be aware of biases in model responses to Yes/No questions.
- Consider the impact of question phrasing on model performance.
- Explore ways to improve model training to enhance logical reasoning.
Full Summary
Logical reasoning with expressions of uncertainty is crucial in various fields, including medicine and law. A benchmark was created to assess LLMs' competence in logical inference over probability operators, using 14,320 procedurally-generated English prompts across fifteen templates.
The evaluation of 29 models showed that most exhibited biases towards answering Yes or No, regardless of the logical structure of the questions. Only 9 models surpassed random chance in accuracy, highlighting a competence floor.
Additionally, variations in question form, verb phrases, and the gender and origin of names in prompts revealed biases across all tested dimensions. These findings suggest that current LLMs struggle with principled reasoning, raising concerns about their reliability in high-stakes applications.