11 papers
AI translation of literary texts is "fine", but readers still prefer human translations
Yves Ferstler, Adam Podoxin, Ty Brassington +3
AI translation of literary works is increasingly common. While the content may be rendered adequately, we do not know enough about how readers experience it in terms of immersivene…
AI use in American newspapers is widespread, uneven, and rarely disclosed
Jenna Russell, Marzena Karpinska, Destiny Akinode +4
AI is rapidly transforming journalism, but the extent of its use in published newspaper articles remains unclear. We address this gap by auditing a large-scale dataset of 186K arti…
OverThink: Slowdown Attacks on Reasoning LLMs
Abhinav Kumar, Jaechul Roh, Ali Naseh +4
Most flagship language models generate explicit reasoning chains, enabling inference-time scaling. However, producing these reasoning chains increases token usage (i.e., reasoning…
OWL: Probing Cross-Lingual Recall of Memorized Texts via World Literature
Alisha Srivastava, Emir Korukluoglu, Minh Nhat Le +4
Large language models (LLMs) are known to memorize and recall English text from their pretraining data. However, the extent to which this ability generalizes to non-English languag…
One ruler to measure them all: Benchmarking multilingual long-context language models
Yekyung Kim, Jenna Russell, Marzena Karpinska +1
We present ONERULER, a multilingual benchmark designed to evaluate long-context language models across 26 languages. ONERULER adapts the English-only RULER benchmark (Hsieh et al.,…
Does quantization affect models' performance on long-context tasks?
Anmol Mekala, Anirudh Atmakuru, Yixiao Song +2
Large language models (LLMs) now support context windows exceeding 128K tokens, but this comes with significant memory requirements and high inference latency. Quantization can mit…