arXiv AI October 26, 2026 By Seiya Tokui, Shoichiro Yamaguchi, Sosuke Kobayashi

Head Dimension Matters in Language Models for Long-Context Retrieval

The paper investigates how the dimension of attention heads influences long‑context retrieval in language models. Experiments show that while pretraining loss and short‑context tasks are largely unaffected, wider heads significantly improve retrieval performance at fixed model sizes. Ablation studies indicate that this benefit persists even when attention weights are frozen, suggesting that wider heads enhance adaptation of other model components such as embeddings and MLPs.

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