9 citations · 13 across the 6 of their papers we have counts for
7 papers · 1 filter
Independent-Component-Based Encoding Models of Brain Activity During Story Comprehension
Kamya Hari, Taha Binhuraib, Jin Li +2
Encoding models provide a powerful framework for linking continuous stimulus features to neural activity; however, traditional voxelwise approaches are limited by measurement noise…
What does it mean to understand language?
Colton Casto, Anna Ivanova, Evelina Fedorenko +1
Language understanding entails not just extracting the surface-level meaning of the linguistic input, but constructing rich mental models of the situation it describes. Here we pro…
How Do LLMs Use Their Depth?
Akshat Gupta, Jay Yeung, Gopala Anumanchipalli +1
Growing evidence suggests that large language models do not use their depth uniformly, yet we still lack a fine-grained understanding of their layer-wise prediction dynamics. In th…
LITcoder: A General-Purpose Library for Building and Comparing Encoding Models
Taha Binhuraib, Ruimin Gao, Anna A. Ivanova
We introduce LITcoder, an open-source library for building and benchmarking neural encoding models. Designed as a flexible backend, LITcoder provides standardized tools for alignin…
Log Probabilities Are a Reliable Estimate of Semantic Plausibility in Base and Instruction-Tuned Language Models
Carina Kauf, Emmanuele Chersoni, Alessandro Lenci +2
Semantic plausibility (e.g. knowing that "the actor won the award" is more likely than "the actor won the battle") serves as an effective proxy for general world knowledge. Languag…
A Better Way to Do Masked Language Model Scoring
Carina Kauf, Anna Ivanova
Estimating the log-likelihood of a given sentence under an autoregressive language model is straightforward: one can simply apply the chain rule and sum the log-likelihood values f…