activity
20232026
most citedCalibrated Seq2seq Models for Efficient and Generalizable Ultra-fine Entity Typing

1 citations · 2 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CL2026★ 1 cited

From sunblock to softblock: Analyzing the correlates of neology in published writing and on social media

Maria Ryskina, Matthew R. Gormley, Kyle Mahowald +3

Living languages are shaped by a host of conflicting internal and external evolutionary pressures. While some of these pressures are universal across languages and cultures, others…

cs.CL2025

Transformer-Enabled Diachronic Analysis of Vedic Sanskrit: Neural Methods for Quantifying Types of Language Change

Ananth Hariharan, David Mortensen

This study demonstrates how hybrid neural-symbolic methods can yield significant new insights into the evolution of a morphologically rich, low-resource language. We challenge the…

cs.CL2025

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…

cs.CL2025

Programming by Examples Meets Historical Linguistics: A Large Language Model Based Approach to Sound Law Induction

Atharva Naik, Darsh Agrawal, Hong Sng +8

Historical linguists have long written "programs" that convert reconstructed words in an ancestor language into their attested descendants via ordered string rewrite functions (cal…

cs.CL2023★ 1 cited

Calibrated Seq2seq Models for Efficient and Generalizable Ultra-fine Entity Typing

Yanlin Feng, Adithya Pratapa, David R Mortensen

Ultra-fine entity typing plays a crucial role in information extraction by predicting fine-grained semantic types for entity mentions in text. However, this task poses significant…