7 papers
LiSeCo: Linear Semantic Control for Language Generation
Emily Cheng, Carmen Amo Alonso
The prevalence of Large Language Models (LLMs) in critical applications highlights the need for controlled language generation methods that are both computationally efficient and e…
Tracing the complexity profiles of different linguistic phenomena through the intrinsic dimension of LLM representations
Marco Baroni, Emily Cheng, Iria de-Dios-Flores +1
We explore intrinsic dimension (ID) of LLM representations as a marker of linguistic complexity. Specifically, we test whether ID differences across model layers reflect well-known…
Abstraction Induces the Brain Alignment of Language and Speech Models
Emily Cheng, Aditya R. Vaidya, Richard Antonello
Research has repeatedly demonstrated that intermediate hidden states extracted from large language models and speech audio models predict measured brain response to natural languag…
GenCtrl -- A Formal Controllability Toolkit for Generative Models
Emily Cheng, Carmen Amo Alonso, Federico Danieli +4
As generative models become ubiquitous, there is a critical need for fine-grained control over the generation process. Yet, while controlled generation methods from prompting to fi…
Principles of semantic and functional efficiency in grammatical patterning
Emily Cheng, Francesca Franzon
Grammatical features such as number and gender serve two central functions in human languages. While they encode salient semantic attributes like numerosity and animacy, they also…
Geometric Signatures of Compositionality Across a Language Model's Lifetime
Jin Hwa Lee, Thomas Jiralerspong, Lei Yu +2
By virtue of linguistic compositionality, few syntactic rules and a finite lexicon can generate an unbounded number of sentences. That is, language, though seemingly high-dimension…