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LM surprisal predicts Chinese reading times, study finds

By PulseAugur Editorial · [1 sources] ·

A new study published on arXiv analyzes the predictive power of language model (LM) surprisal on Chinese reading times. Researchers developed the Shortest Matching Sequence (SMS) alignment scheme to bridge discrepancies between eye-tracking corpora and LM subword tokenization. Using Chinese-Pythia models, the study found that LM surprisal can predict reading times, though its effectiveness varies by corpus and model size, with some instances showing inverse scaling. AI

IMPACT This research suggests that language model surprisal can be a useful metric for understanding reading comprehension, with implications for developing more nuanced NLP models for Chinese.

RANK_REASON The cluster contains a research paper detailing a systematic analysis of LM surprisal in reading Chinese. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LM surprisal predicts Chinese reading times, study finds

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The cluster contains a research paper detailing a systematic analysis of LM surprisal in reading Chinese. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Hongao Zhu, Muxiaoqiao Xu, Yikang Liu, Siyuan Song, Yuxia Wang, Byung-Doh Oh, Hai Hu ·

    A Systematic Analysis of the Predictive Power of LM Surprisal in Reading Chinese

    arXiv:2610.04898v2 Announce Type: replace-cross Abstract: This study analyzes the predictive power of LM-derived, token-level surprisal on Mandarin Chinese reading times. We first propose the Shortest Matching Sequence (SMS), an alignment scheme that maps between the word segment…

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