52 citations · 82 across the 4 of their papers we have counts for
5 papers
Extracting Latent Steering Vectors from Pretrained Language Models
Nishant Subramani, Nivedita Suresh, Matthew E. Peters
Prior work on controllable text generation has focused on learning how to control language models through trainable decoding, smart-prompt design, or fine-tuning based on a desired…
The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
Sebastian Gehrmann, Tosin Adewumi, Karmanya Aggarwal +53
We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics. Measuring progress in NLG relies on a constantly evolving ecosystem of auto…
A Survey of Deep Learning Approaches for OCR and Document Understanding
Nishant Subramani, Alexandre Matton, Malcolm Greaves +1
Documents are a core part of many businesses in many fields such as law, finance, and technology among others. Automatic understanding of documents such as invoices, contracts, and…
Discovering Useful Sentence Representations from Large Pretrained Language Models
Nishant Subramani, Nivedita Suresh
Despite the extensive success of pretrained language models as encoders for building NLP systems, they haven't seen prominence as decoders for sequence generation tasks. We explore…
Can Unconditional Language Models Recover Arbitrary Sentences?
Nishant Subramani, Samuel R. Bowman, Kyunghyun Cho
Neural network-based generative language models like ELMo and BERT can work effectively as general purpose sentence encoders in text classification without further fine-tuning. Is…