Ananya Harsh Jha — accepted AI-conference papers

2024

Paloma: A Benchmark for Evaluating Language Model Fit

Ian Magnusson, Akshita Bhagia, Valentin Hofmann, Luca Soldaini, Ananya Harsh Jha, Oyvind Tafjord +10

NeurIPS 2024poster

Evaluations of language models (LMs) commonly report perplexity on monolithic data held out from training. Implicitly or explicitly, this data is composed of domains—varying distributions of language. We introduce Perplexity Analysis for Language Model Assessment (Paloma), a benchmark to measure LM…

Cited by 7 Source ↗PDF ↗Scholar ↗

2024

Scalable Data Ablation Approximations for Language Models through Modular Training and Merging

Clara Na, Ian Magnusson, Ananya Harsh Jha, Tom Sherborne, Emma Strubell, Jesse Dodge +1

EMNLP 2024main

Training data compositions for Large Language Models (LLMs) can significantly affect their downstream performance. However, a thorough data ablation study exploring large sets of candidate data mixtures is typically prohibitively expensive since the full effect is seen only after training the models…

Cited by 4 Source ↗PDF ↗code ↗Scholar ↗

2018

Disentangling Factors of Variation with Cycle-Consistent Variational Auto-Encoders

Ananya Harsh Jha, Saket Anand, Maneesh Singh, VSR Veeravasarapu

ECCV 2018poster

Generative models that learn disentangled representations for different factors of variation in an image can be very useful for targeted data augmentation. By sampling from the disentangled latent subspace of interest, we can efficiently generate new data necessary for a particular task. Learning di…

Cited by 163 Source ↗PDF ↗Scholar ↗