With the rise of large language models (LLMs), computational methods are becoming increasingly popular in psycholinguistics. This post gathers key resources on using LLM-based surprisal for syntactic ambiguity research, as well as tutorials on training and evaluating these models.
Word surprisal is a commonly used indicator of processing difficulty. Check out a unified interface for computing surprisal from language models and tools for calculating psycholinguistically-relevant metrics of language statistics using transformer language models.
Check out this paper for a large-scale dataset on using LLMs surprisal to explain syntactic disambiguation difficulty: Syntactic Ambiguity Processing Benchmark
This tutorials and resources on LLMs training and evaluation from Professor Suhas Arehalli can be helpful in model training and manipulation.