2 citations · 5 across the 20 of their papers we have counts for
7 papers · 1 filter
WinoPron: Revisiting English Winogender Schemas for Consistency, Coverage, and Grammatical Case
Vagrant Gautam, Julius Steuer, Eileen Bingert +3
While measuring bias and robustness in coreference resolution are important goals, such measurements are only as good as the tools we use to measure them. Winogender Schemas (Rudin…
From Insights to Actions: The Impact of Interpretability and Analysis Research on NLP
Marius Mosbach, Vagrant Gautam, Tomás Vergara-Browne +2
Interpretability and analysis (IA) research is a growing subfield within NLP with the goal of developing a deeper understanding of the behavior or inner workings of NLP systems and…
Understanding "Democratization" in NLP and ML Research
Arjun Subramonian, Vagrant Gautam, Dietrich Klakow +1
Recent improvements in natural language processing (NLP) and machine learning (ML) and increased mainstream adoption have led to researchers frequently discussing the "democratizat…
Stop! In the Name of Flaws: Disentangling Personal Names and Sociodemographic Attributes in NLP
Vagrant Gautam, Arjun Subramonian, Anne Lauscher +1
Personal names simultaneously differentiate individuals and categorize them in ways that are important in a given society. While the natural language processing community has thus…
Robust Pronoun Fidelity with English LLMs: Are they Reasoning, Repeating, or Just Biased?
Vagrant Gautam, Eileen Bingert, Dawei Zhu +2
Robust, faithful and harm-free pronoun use for individuals is an important goal for language model development as their use increases, but prior work tends to study only one or two…
What explains the success of cross-modal fine-tuning with ORCA?
Paloma García-de-Herreros, Vagrant Gautam, Philipp Slusallek +2
ORCA (Shen et al., 2023) is a recent technique for cross-modal fine-tuning, i.e., applying pre-trained transformer models to modalities beyond their training data. The technique co…
The Impact of Demonstrations on Multilingual In-Context Learning: A Multidimensional Analysis
Miaoran Zhang, Vagrant Gautam, Mingyang Wang +4
In-context learning is a popular inference strategy where large language models solve a task using only a few labeled demonstrations without needing any parameter updates. Although…