73 citations · 144 across the 9 of their papers we have counts for
14 papers · 1 filter
A Taxonomy of Ambiguity Types for NLP
Margaret Y. Li, Alisa Liu, Zhaofeng Wu +1
Ambiguity is an critical component of language that allows for more effective communication between speakers, but is often ignored in NLP. Recent work suggests that NLP systems may…
Third-Party Language Model Performance Prediction from Instruction
Rahul Nadkarni, Yizhong Wang, Noah A. Smith
Language model-based instruction-following systems have lately shown increasing performance on many benchmark tasks, demonstrating the capability of adapting to a broad variety of…
Time is Encoded in the Weights of Finetuned Language Models
Kai Nylund, Suchin Gururangan, Noah A. Smith
We present time vectors, a simple tool to customize language models to new time periods. Time vectors are created by finetuning a language model on data from a single time (e.g., a…
That was the last straw, we need more: Are Translation Systems Sensitive to Disambiguating Context?
Jaechan Lee, Alisa Liu, Orevaoghene Ahia +2
The translation of ambiguous text presents a challenge for translation systems, as it requires using the surrounding context to disambiguate the intended meaning as much as possibl…
Efficiency Pentathlon: A Standardized Arena for Efficiency Evaluation
Hao Peng, Qingqing Cao, Jesse Dodge +11
Rising computational demands of modern natural language processing (NLP) systems have increased the barrier to entry for cutting-edge research while posing serious environmental co…
Stubborn Lexical Bias in Data and Models
Sofia Serrano, Jesse Dodge, Noah A. Smith
In NLP, recent work has seen increased focus on spurious correlations between various features and labels in training data, and how these influence model behavior. However, the pre…