1 citations · 1 across the 6 of their papers we have counts for
12 papers
What Can String Probability Tell Us About Grammaticality?
Jennifer Hu, Ethan Gotlieb Wilcox, Siyuan Song +2
What have language models (LMs) learned about grammar? This question remains hotly debated, with major ramifications for linguistic theory. However, since probability and grammatic…
Convergence and Divergence of Language Models under Different Random Seeds
Finlay Fehlauer, Kyle Mahowald, Tiago Pimentel
In this paper, we investigate the convergence of language models (LMs) trained under different random seeds, measuring convergence as the expected per-token Kullback--Leibler (KL)…
Privileged Self-Access Matters for Introspection in AI
Siyuan Song, Harvey Lederman, Jennifer Hu +1
Whether AI models can introspect is an increasingly important practical question. But there is no consensus on how introspection is to be defined. Beginning from a recently propose…
semantic-features: A User-Friendly Tool for Studying Contextual Word Embeddings in Interpretable Semantic Spaces
Jwalanthi Ranganathan, Rohan Jha, Kanishka Misra +1
We introduce semantic-features, an extensible, easy-to-use library based on Chronis et al. (2023) for studying contextualized word embeddings of LMs by projecting them into interpr…
Is It JUST Semantics? A Case Study of Discourse Particle Understanding in LLMs
William Sheffield, Kanishka Misra, Valentina Pyatkin +3
Discourse particles are crucial elements that subtly shape the meaning of text. These words, often polyfunctional, give rise to nuanced and often quite disparate semantic/discourse…
A suite of LMs comprehend puzzle statements as well as humans
Adele E Goldberg, Supantho Rakshit, Jennifer Hu +1
Recent claims suggest that large language models (LMs) underperform humans in comprehending minimally complex English statements (Dentella et al., 2024). Here, we revisit those fin…