151 citations · 171 across the 11 of their papers we have counts for
3 papers · 1 filter
Perturb Your Data: Paraphrase-Guided Training Data Watermarking
Pranav Shetty, Mirazul Haque, Petr Babkin +3
Training data detection is critical for enforcing copyright and data licensing, as Large Language Models (LLM) are trained on massive text corpora scraped from the internet. We pre…
CoCoLex: Confidence-guided Copy-based Decoding for Grounded Legal Text Generation
Santosh T. Y. S. S, Youssef Tarek Elkhayat, Oana Ichim +5
Due to their ability to process long and complex contexts, LLMs can offer key benefits to the Legal domain, but their adoption has been hindered by their tendency to generate unfai…
Where is this coming from? Making groundedness count in the evaluation of Document VQA models
Armineh Nourbakhsh, Siddharth Parekh, Pranav Shetty +3
Document Visual Question Answering (VQA) models have evolved at an impressive rate over the past few years, coming close to or matching human performance on some benchmarks. We arg…