1 citations · 2 across the 9 of their papers we have counts for
9 papers
Apollo Restore: A Foundation LLM for Historical Greek Optimized for Fill-in-the-Middle Restoration of Ancient Greek Texts
Hope McGovern, Anna Dolganov, Samuel Belkadi +3
We present Apollo Restore, a 24-billion-parameter large language model for restoring lacunae---physical gaps---in fragmentary Ancient Greek texts. Fine-tuned from Mistral Small wit…
Shieldstral
Antonia Calvi, Avinash Sooriyarachchi, Giada Pistilli +273
We introduce Shieldstral, a 3B-parameter policy-adaptive multimodal safety classifier that matches or outperforms models nearly 7 its size on text safety benchmarks and set…
Voxtral TTS
Mistral-AI, :, Alexander H. Liu +186
We introduce Voxtral TTS, an expressive multilingual text-to-speech model that generates natural speech from as little as 3 seconds of reference audio. Voxtral TTS adopts a hybrid…
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…
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…
Your Large Language Models Are Leaving Fingerprints
Hope McGovern, Rickard Stureborg, Yoshi Suhara +1
It has been shown that finetuned transformers and other supervised detectors effectively distinguish between human and machine-generated text in some situations arXiv:2305.13242, b…
CLIMB: Curriculum Learning for Infant-inspired Model Building
Richard Diehl Martinez, Zebulon Goriely, Hope McGovern +4
We describe our team's contribution to the STRICT-SMALL track of the BabyLM Challenge. The challenge requires training a language model from scratch using only a relatively small t…
A Source-Criticism Debiasing Method for GloVe Embeddings
Hope McGovern
It is well-documented that word embeddings trained on large public corpora consistently exhibit known human social biases. Although many methods for debiasing exist, almost all fix…