7 papers · 1 filter
When Models Know More Than They Say: Probing Analogical Reasoning in LLMs
Hope McGovern, Caroline Craig, Thomas Lippincott +1
Analogical reasoning is a core cognitive faculty essential for narrative understanding. While LLMs perform well when surface and structural cues align, they struggle in cases where…
Rashid: A Cipher-Based Framework for Exploring In-Context Language Learning
Niyati Bafna, Ryan Soh-Eun Shim, Barbara Plank +2
Where there is growing interest in in-context language learning (ICLL) for unseen languages with large language models, such languages usually suffer from the lack of NLP tools, da…
The Translation Barrier Hypothesis: Multilingual Generation with Large Language Models Suffers from Implicit Translation Failure
Niyati Bafna, Tianjian Li, Kenton Murray +4
Multilingual generation with large language models (LLMs) is often of poor quality for mid- to low-resource languages, but the causes for this are not well-understood. We first dem…
Computational Discovery of Chiasmus in Ancient Religious Text
Hope McGovern, Hale Sirin, Tom Lippincott
Chiasmus, a debated literary device in Biblical texts, has captivated mystics while sparking ongoing scholarly discussion. In this paper, we introduce the first computational appro…
Characterizing the Effects of Translation on Intertextuality using Multilingual Embedding Spaces
Hope McGovern, Hale Sirin, Tom Lippincott
Rhetorical devices are difficult to translate, but they are crucial to the translation of literary documents. We investigate the use of multilingual embedding spaces to characteriz…
DialUp! Modeling the Language Continuum by Adapting Models to Dialects and Dialects to Models
Niyati Bafna, Emily Chang, Nathaniel R. Robinson +4
Most of the world's languages and dialects are low-resource, and lack support in mainstream machine translation (MT) models. However, many of them have a closely-related high-resou…