4 citations · 11 across the 9 of their papers we have counts for
9 papers
Predicting Deep Neural Network Training Outcomes from Early Training Telemetry
Ranjita Naik, Anh D. Nguyen, Pankaj Kumar Singh
Large hyperparameter sweeps for deep neural networks spend substantial compute on configurations that are effectively doomed from the first few epochs. We study whether a single tr…
GATE X-E : A Challenge Set for Gender-Fair Translations from Weakly-Gendered Languages
Spencer Rarrick, Ranjita Naik, Sundar Poudel +1
Neural Machine Translation (NMT) continues to improve in quality and adoption, yet the inadvertent perpetuation of gender bias remains a significant concern. Despite numerous studi…
Evaluating Gender Bias in the Translation of Gender-Neutral Languages into English
Spencer Rarrick, Ranjita Naik, Sundar Poudel +1
Machine Translation (MT) continues to improve in quality and adoption, yet the inadvertent perpetuation of gender bias remains a significant concern. Despite numerous studies into…
Reducing Gender Bias in Machine Translation through Counterfactual Data Generation
Ranjita Naik, Spencer Rarrick, Vishal Chowdhary
Recent advances in neural methods have led to substantial improvement in the quality of Neural Machine Translation (NMT) systems. However, these systems frequently produce translat…
KITAB: Evaluating LLMs on Constraint Satisfaction for Information Retrieval
Marah I Abdin, Suriya Gunasekar, Varun Chandrasekaran +5
We study the ability of state-of-the art models to answer constraint satisfaction queries for information retrieval (e.g., 'a list of ice cream shops in San Diego'). In the past, s…
Diversity of Thought Improves Reasoning Abilities of LLMs
Ranjita Naik, Varun Chandrasekaran, Mert Yuksekgonul +2
Large language models (LLMs) are documented to struggle in settings that require complex reasoning. Nevertheless, instructing the model to break down the problem into smaller reaso…
Attention Satisfies: A Constraint-Satisfaction Lens on Factual Errors of Language Models
Mert Yuksekgonul, Varun Chandrasekaran, Erik Jones +5
We investigate the internal behavior of Transformer-based Large Language Models (LLMs) when they generate factually incorrect text. We propose modeling factual queries as constrain…
Social Biases through the Text-to-Image Generation Lens
Ranjita Naik, Besmira Nushi
Text-to-Image (T2I) generation is enabling new applications that support creators, designers, and general end users of productivity software by generating illustrative content with…
GATE: A Challenge Set for Gender-Ambiguous Translation Examples
Spencer Rarrick, Ranjita Naik, Varun Mathur +2
Although recent years have brought significant progress in improving translation of unambiguously gendered sentences, translation of ambiguously gendered input remains relatively u…