- Paper: Neural decoding + Transformer + minimal pairs = new syntactic probing approach for LLMs
MA student Linyang He leads a team that advances probing methods for large language models by combining a linear decoder with the BLiMP large-scale benchmark of linguistic minimal pairs. The result is a probing method that isolate patterns of layer-wise […]
- Dr. Tzu-yun Tung defends dissertation, on to Chicago!
Many congratulations to Tzu-yun who has successfully defended her dissertation Prediction and Memory Retrieval during Dependency Resolution. The work combines electroencephalography, syntax, cognitive psychology and computational modeling to characterize how predictive mechanisms interact with memory retrieval – through the guise […]
- Reproducing the Alice analyses
One of our projects last summer was to go back into the archives and dig out the code used for data analyses in all our papers published that use the Alice in Wonderland EEG datasets. That code has been tidied […]
- Paper: Tung on prediction and memory retrieval
Tzu-Yun Tung publishes the first paper from her dissertation! Combining ERPs with an experimental design using NP-ellipsis in Mandarin, this work tests how interference effects in memory retrieval can be ameliorated by predictability. The particular pattern of amelioration – most […]
- Lab presentations at SNL2023: See you in Marseille
Lab members will be presenting on decoding verb phrase construction, memory retreival during naturalistic comprehension, and morphological decomposition. We hope to see you there! Tung, Tzu-Yun Modeling memory retrieval during naturalistic comprehension. (Poster A46, Poster Session A Tuesday, October 24, […]
- Jeonghwa Cho presents at AMLaP 2023
Do you need another excuse to visit San Sebastián, Spain for AMLaP 2023? Jeonghwa Cho will be presenting on two exciting projects: Poster: Sat Sep 2 14:00-15:10 Cross-language masked prefix priming for early and late bilinguals. (co-author Jonathan Brennan) Talk: […]
- Paper: Comparing parsing strategies for a head-final language with RNNG
Yushi Sugimoto (UMich PhD 2022) and Yohei Oseki lead this paper demonstrating an advantage for left-corner parsing, as implemented with their updated RNNG, to capture neural signals while participants read Japanese newspaper text. This is the first paper, to my […]
