7 citations · 7 across the 10 of their papers we have counts for
26 papers · 1 filter
How Anthropomorphic Language Impacts Public Perceptions of AI
Betty Li Hou, Sophie Hao, Sunoo Park +1
Public discourse about artificial intelligence (AI) often uses anthropomorphic language: language that attributes human capabilities and characteristics to the system. This practic…
To model human linguistic prediction, make LLMs less superhuman
Byung-Doh Oh, Tal Linzen
When we read, we make predictions about upcoming words; these predictions influence our reading behavior. The success of large language models (LLMs), which, like humans, make pred…
Simulating Human Memory with Language Models
Qihan Wang, Nicholas Tomlin, Michael Hu +2
Language models are increasingly being deployed as user simulators, but their memory is far more reliable than that of real users. To measure this gap, we run a series of classic m…
Why are language models less surprised than humans? Testing the Parse Multiplicity Mismatch Hypothesis
William Timkey, Brian Dillon, Tal Linzen
Surprisal theory posits that the processing difficulty of a word is determined by its predictability in context, offering a potential link between human sentence processing and nex…
Always Learning, Always Mixing: Efficient and Simple Data Mixing All The Time
Michael Y. Hu, Apurva Gandhi, Kyunghyun Cho +2
Data mixing decides how to combine different sources or types of data and is a consequential problem throughout language model training. In pretraining, data composition is a key d…
Language Models Struggle to Use Representations Learned In-Context
Michael A. Lepori, Tal Linzen, Ann Yuan +1
Though large language models (LLMs) have enabled great success across a wide variety of tasks, they still appear to fall short of one of the loftier goals of artificial intelligenc…