91 citations · 165 across the 33 of their papers we have counts for
12 papers · 1 filter
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…
Causal Interventions Reveal Shared Structure Across English Filler-Gap Constructions
Sasha Boguraev, Christopher Potts, Kyle Mahowald
Language Models (LMs) have emerged as powerful sources of evidence for linguists seeking to develop theories of syntax. In this paper, we argue that causal interpretability methods…
On Language Models' Sensitivity to Suspicious Coincidences
Sriram Padmanabhan, Kanishka Misra, Kyle Mahowald +1
Humans are sensitive to suspicious coincidences when generalizing inductively over data, as they make assumptions as to how the data was sampled. This results in smaller, more spec…
Both Direct and Indirect Evidence Contribute to Dative Alternation Preferences in Language Models
Qing Yao, Kanishka Misra, Leonie Weissweiler +1
Language models (LMs) tend to show human-like preferences on a number of syntactic phenomena, but the extent to which these are attributable to direct exposure to the phenomena or…
Language Models Fail to Introspect About Their Knowledge of Language
Siyuan Song, Jennifer Hu, Kyle Mahowald
There has been recent interest in whether large language models (LLMs) can introspect about their own internal states. Such abilities would make LLMs more interpretable, and also v…
Constructions are Revealed in Word Distributions
Joshua Rozner, Leonie Weissweiler, Kyle Mahowald +1
Construction grammar posits that constructions, or form-meaning pairings, are acquired through experience with language (the distributional learning hypothesis). But how much infor…
Linguistic Generalizations are not Rules: Impacts on Evaluation of LMs
Leonie Weissweiler, Kyle Mahowald, Adele Goldberg
Linguistic evaluations of how well LMs generalize to produce or understand language often implicitly take for granted that natural languages are generated by symbolic rules. Accord…