The Cognitive Cost of LLM Writing: A Reading-Time Comparison with Human Text
Although linguistic differences between human-written and Large Language Model (LLM)-generated texts have been extensively studied, their implications for the cognitive effort involved in reading remain less explored.
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Key points
- In psycholinguistics, reading time is commonly used as an indirect measure of cognitive processing effort.
- Building on this perspective, we investigate whether human-written texts and their LLM-generated rewrites exhibit differences in predicted reading time that may reflect differences in processing demands for readers.
- To this end, we introduce RoBERTa-RT, a model trained on human eye-tracking data to predict word-level reading times.
- From a cognitive perspective, our findings suggest that LLM-generated writing may redistribute reading demands: reducing the amount of text to process while increasing the predicted processing effort associated with individual words.
Sources (1)
- [1]The Cognitive Cost of LLM Writing: A Reading-Time Comparison with Human TextarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 04:35 PM
Although linguistic differences between human-written and Large Language Model (LLM)-generated texts have been extensively studied, their implications for the cognitive effort involved in reading remain less explored.
In psycholinguistics, reading time is commonly used as an indirect measure of cognitive processing effort.
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