Autoregressive large language models (LLMs) process text token-by-token, yet the human language system operates over multi-word units. We ask whether aggregating LLM representations at the phrase level yields a closer correspondence to human reading behavior and language cortex than the default word-level representations, and whether phrase-segmentation fine-tuning amplifies this correspondence. Using Meta-Llama-3.1-8B (base and fine-tuned), we provide three converging lines of evidence. First, phrase-level attention features predict regressive eye-saccade patterns more closely than word-level features; a partial correlation analysis with a shuffled-boundary control indicates that this is not solely an aggregation artifact and that linguistic chunk boundaries explain unique variance beyond word-level attention. Second, fMRI encoding analyses show that fine-tuning selectively improves phrase encoding in left superior temporal gyrus and inferior frontal gyrus, with no improvement for word representations. Third, representational similarity analysis confirms a phrase-specific gain in model-brain geometric alignment. These results identify phrase-level representation as a critical granularity for LLM–human correspondence and suggest that targeted training can model human-like compositional processing, linking computational representations to hierarchical theories of language.