Does Episodic Memory Help Close the Lexical Frequency Gap in Sensitivity to Syntactic Contrasts? A Test Using Retrieval-Augmented Language Models

arXiv:2608.23851 · cs.CL · Submitted 2026-08-24 · Read on arXiv

cs.CL

Submitted: 2026-08-24

Updated: 2026-09-08

Code: https://github.com/Jing-L97/Generative_replay

Terminology

• Relative Clauses — 14× in this paper
• Adam — 9× in this paper · explained in 2 episodes
• Accuracy — 8× in this paper · explained in 1 episode
• Complementary Learning Systems — 6× in this paper
• Robustness — 6× in this paper · explained in 5 episodes
• Transformer — 6× in this paper · explained in 2 episodes
• Benchmark — 5× in this paper · explained in 2 episodes
• Embeddings — 5× in this paper · explained in 2 episodes
• Perplexity — 5× in this paper · explained in 3 episodes
• Generalization — 4× in this paper · explained in 13 episodes
• Empirical Methods in Natural Language Processing — 3× in this paper
• Semantic Similarity — 3× in this paper · explained in 2 episodes
• Adaptive Mechanism — 1× in this paper · explained in 1 episode
• Anisotropy — 1× in this paper · explained in 3 episodes
• Compositional Generalization — 1× in this paper · explained in 4 episodes
• In-Context Learning — 1× in this paper · explained in 4 episodes
• Pruning — 1× in this paper · explained in 4 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Transformer Models — 1× in this paper · explained in 2 episodes

Sources

• FairyTailor: A Multimodal Generative Framework for Storytelling
• Transformers generalize differently from information stored in context vs in weights
• Uncontrolled Lexical Exposure Leads to Overestimation of Compositional Generalization in Pretrained Models
• Latent learning: episodic memory complements parametric learning by enabling flexible reuse of experiences

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