Beyond Memorization: Assessing Semantic Generalization in Large Language Models Using Phrasal Constructions
arXiv:2501.04661v3 Announce Type: replace-cross Abstract: The web-scale of pretraining data has created an important evaluation challenge: to disentangle linguistic competence on cases well-represented in pretraining data from generalization to out-of-domain language, specifically the dynamic, real-world instances less common in pretraining data. To this end, we construct a diagnostic evaluation to systematically assess natural language understanding in LLMs by leveraging Construction Grammar (CxG). CxG provides a psycholinguistically grounded framework for testing generalization, as it explic
The accelerating deployment of LLMs highlights the urgent need for robust evaluation methods to understand their true capabilities beyond mere data regurgitation.
This research provides a more sophisticated framework for assessing LLM generalization, moving beyond simple memorization, which is critical for future AI development and trustworthiness.
The focus for evaluating LLMs shifts towards semantic understanding and generalization to out-of-domain linguistic structures, rather than solely relying on performance on training data.
- · AI researchers focusing on generalization
- · Developers building more robust LLMs
- · Industries relying on reliable AI understanding
- · LLM evaluators using simplistic memorization tests
- · Models that primarily rely on rote learning from massive datasets
Improved diagnostic evaluations lead to more accurate assessments of LLM capabilities and limitations.
This understanding will guide the development of genuinely generalized and less brittle AI models.
More capable and trustworthy LLMs could accelerate the adoption of complex AI applications across various sectors, potentially enabling advanced AI agents.
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Read at arXiv cs.AI