2 citations · 2 across the 6 of their papers we have counts for
6 papers
ÜberWeb: Insights from Multilingual Curation for a 20-Trillion-Token Dataset
DatologyAI, :, Aldo Gael Carranza +32
Multilinguality is a core capability for modern foundation models, yet training high-quality multilingual models remains challenging due to uneven data availability across language…
DatBench: Discriminative, Faithful, and Efficient VLM Evaluations
DatologyAI, :, Siddharth Joshi +30
Empirical evaluation serves as the primary compass guiding research progress in foundation models. Despite a large body of work focused on training frontier vision-language models…
Luxical: High-Speed Lexical-Dense Text Embeddings
DatologyAI, :, Luke Merrick +31
Frontier language model quality increasingly hinges on our ability to organize web-scale text corpora for training. Today's dominant tools trade off speed and flexibility: lexical…
BeyondWeb: Lessons from Scaling Synthetic Data for Trillion-scale Pretraining
DatologyAI, :, Pratyush Maini +28
Recent advances in large language model (LLM) pretraining have shown that simply scaling data quantity eventually leads to diminishing returns, hitting a data wall. In response, th…
MATES: Model-Aware Data Selection for Efficient Pretraining with Data Influence Models
Zichun Yu, Spandan Das, Chenyan Xiong
Pretraining data selection has the potential to improve language model pretraining efficiency by utilizing higher-quality data from massive web data corpora. Current data selection…
TLDR at SemEval-2024 Task 2: T5-generated clinical-Language summaries for DeBERTa Report Analysis
Spandan Das, Vinay Samuel, Shahriar Noroozizadeh
This paper introduces novel methodologies for the Natural Language Inference for Clinical Trials (NLI4CT) task. We present TLDR (T5-generated clinical-Language summaries for DeBERT…