11 citations · 11 across the 4 of their papers we have counts for
5 papers · 1 filter
Revisiting LLM Homogeneity Through a Multidimensional Lens
Ziliang Qiu, Renfen Hu, Yi Ding +1
Concerns about LLM homogeneity are typically quantified with embedding similarity, which reduces two texts to a single score. We revisit homogeneity with 25 interpretable metrics s…
From Utterances to Networks: Modelling Slang Adoption and Diffusion Across Subreddits
Xiaoning Wang, Ted Underwood, Zhewei Sun
Adoption and diffusion of neologisms in online communities have received renewed attention in recent years. As internet slang terms such as APT, referring to a K-pop song, and phra…
Chronologic: Measuring Language Models' Ability to Represent the Past
Ted Underwood, Ziliang Qiu, Sarah Griebel +4
Language models are appealing tools for research on the past. But to trust the evidence a model provides, researchers need to know whether its responses fit the period represented.…
Fluency and Faithfulness in Human and Machine Literary Translation
Sarah Griebel, Ted Underwood
Literary translation requires balancing target-language fluency with faithfulness to the source. Recent large language models (LLMs) often produce fluent translations, but it remai…
Can Language Models Represent the Past without Anachronism?
Ted Underwood, Laura K. Nelson, Matthew Wilkens
Before researchers can use language models to simulate the past, they need to understand the risk of anachronism. We find that prompting a contemporary model with examples of perio…