Surprisal, the negative log probability of a word given its context, is the dominant computational metric for quantifying reading difficulty and a common item difficulty estimator in reading research. Yet how the language model family, surprisal granularity, and corpus type jointly shape the surprisal–reading time link remains unclear. We conducted a secondary analysis of two public English reading corpora: the Natural Stories Corpus (self-paced reading, 181 readers) and the Provo Corpus (eye tracking with cloze norms). Surprisal was computed from GPT 2 (Small, XL), Llama 2 (7B, 13B), and Llama 3 (8B), together with a 5-gram baseline and human cloze norms. Linear mixed-effects models tested the baseline contribution of neural surprisal, inverse scaling within model families, word-level versus sub-word aggregation, and the linking function shape. Neural surprisal contributed reliable variance above a strong baseline in both corpora. A clear inverse scaling pattern emerged: GPT 2 Small produced the largest fit improvements, exceeding GPT 2 XL, Llama 2 13B, and Llama 3 8B. Word-level aggregation outperformed sub-word aggregation, especially for measures of early lexical access. Non-parametric analyses supported an approximately linear linking function, and cloze norms carried information not fully captured by neural surprisal. These findings show that larger models are not automatically better cognitive models of reading and that surprisal granularity is not a neutral analytic choice.
Shuting Liu, Yong Mei· Journal of Intelligence· 0 citations
Speculative decoding accelerates autoregressive generation by allowing a lightweight drafter to propose future tokens while a target model verifies them in parallel. Its lossless guarantee has motivated a line of work that pushes the drafter itself toward parallel generation. The most recent paradigm is block-parallel generative drafting, including diffusion-based methods such as DFlash and DSpark, achieving up to 3.6x speedup on common daily chatting tasks. While this transition is well studied in text-only LLMs, its applicability to multimodal models remains an open question. Existing multimodal speculative decoding efforts focus on input compression, adapter alignment, candidate coverage, or modality-specific verification; however, block-parallel generative drafting remains largely unexplored. To bridge this gap, this paper combines a modality-centered survey with a cross-architecture empirical study to ask: Is multimodal speculative decoding ready for diffusion-based parallel drafting? In this survey, we systematically analyze a wide spectrum of multimodal models, spanning Vision-Language, Video-Language, Audio, and Vision-Language-Action (VLA) architectures, from the dual perspectives of drafting parallelism and cross-modal information interaction. We introduce a unified taxonomy that isolates drafter-side parallelism from orthogonal design choices such as tree construction and verification strategies. Furthermore, we provide a comprehensive empirical comparison of existing methods under varying degrees of parallelism across standardized multimodal benchmarks, including OCR, VQA, visual reasoning, and image captioning. Finally, we summarize the limitations of current approaches, discuss open challenges, and outline promising future directions for this rapidly evolving field.
Yantao Li, Huanlin Gao, Fang Zhao et al.· 0 citations