arXiv:2601.09886v2 Announce Type: replace Abstract: How predictable a word is can be quantified in two ways: using human responses to the cloze task or using probabilities from language models (LMs).When used as predictors of processing effort, LM probabilities outperform probabilities derived from cloze data. However, it is important to establish that LM probabilities do so for the right reasons, since different predictors can lead to different scientific conclusions about the role of prediction in language comprehension. We present evidence for three hypotheses about the advantage of LM prob
The rapid advancement and integration of large language models (LLMs) into various applications necessitate a deeper understanding of their internal mechanisms and how they measure language predictability.
For a strategic reader, insights into why language models outperform human-derived metrics for language processing are crucial for developing more effective AI systems and understanding human-AI cognitive interaction.
This research refines our understanding of language model performance, potentially leading to more accurate proxies for human language comprehension and better benchmarks for AI development.
- · AI researchers
- · NLP developers
- · Cognitive science
- · Traditional cloze task methodologies
- · Less sophisticated language models
This research provides a more robust theoretical basis for understanding the predictive superiority of language models over human-derived cloze data in language processing.
Improved understanding of language model predictions could lead to more efficient and natural human-AI interfaces and educational tools.
These findings might influence the design of future general artificial intelligence by highlighting key mechanisms of language comprehension and generation.
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