Investigating More Explainable and Partition-Free Compositionality Estimation for LLMs: A Rule-Generation Perspective
📰 ArXiv cs.AI
Improve LLM compositionality estimation with explainable and partition-free methods using a rule-generation perspective
Action Steps
- Apply compositional generalization tests to LLMs using rule-generation methods
- Evaluate LLMs' understanding of sample compositionality to improve explainability
- Use partition-free estimation methods to avoid combination leakage issues
- Implement rule-generation algorithms to generate test sets with unseen combinations
- Compare the performance of different compositionality estimation methods using metrics such as accuracy and F1-score
Who Needs to Know This
NLP researchers and engineers working with LLMs can benefit from this approach to improve model explainability and compositionality estimation
Key Insight
💡 Explainable and partition-free compositionality estimation methods can improve LLM performance and trustworthiness
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🤖 Improve LLM compositionality estimation with explainable & partition-free methods! 📊
Key Takeaways
Improve LLM compositionality estimation with explainable and partition-free methods using a rule-generation perspective
Full Article
Title: Investigating More Explainable and Partition-Free Compositionality Estimation for LLMs: A Rule-Generation Perspective
Abstract:
arXiv:2604.27340v1 Announce Type: new Abstract: Compositional generalization tests are often used to estimate the compositionality of LLMs. However, such tests have the following limitations: (1) they only focus on the output results without considering LLMs' understanding of sample compositionality, resulting in explainability defects; (2) they rely on dataset partition to form the test set with combinations unseen in the training set, suffering from combination leakage issues. In this work, we
Abstract:
arXiv:2604.27340v1 Announce Type: new Abstract: Compositional generalization tests are often used to estimate the compositionality of LLMs. However, such tests have the following limitations: (1) they only focus on the output results without considering LLMs' understanding of sample compositionality, resulting in explainability defects; (2) they rely on dataset partition to form the test set with combinations unseen in the training set, suffering from combination leakage issues. In this work, we
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