373 citations · 549 across the 8 of their papers we have counts for
7 papers · 1 filter
A cross-domain tropical species dataset with Chinese vernacular names and CITES source links
Jeff Wang
We describe a versioned cross-domain dataset of 410,499 active tropical species (working snapshot 2026-04-20) spanning three applied subdomains -- tropical_plants, tropical_aquatic…
A Registry-Bound LLM Pipeline for Evidence-Grounded Trait Extraction across Tropical Plants, Aquatic Species, and Exotic Pets
Jeff Wang
We describe a registry-bound large-language-model extraction pipeline producing evidence-grounded structured trait records at scale, on cultivated tropical plant, aquatic, and pet…
BLESS: Benchmarking Large Language Models on Sentence Simplification
Tannon Kew, Alison Chi, Laura Vásquez-Rodríguez +4
We present BLESS, a comprehensive performance benchmark of the most recent state-of-the-art large language models (LLMs) on the task of text simplification (TS). We examine how wel…
OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization
Srinivasan Iyer, Xi Victoria Lin, Ramakanth Pasunuru +15
Recent work has shown that fine-tuning large pre-trained language models on a collection of tasks described via instructions, a.k.a. instruction-tuning, improves their zero and few…
No Language Left Behind: Scaling Human-Centered Machine Translation
NLLB Team, Marta R. Costa-jussà, James Cross +36
Driven by the goal of eradicating language barriers on a global scale, machine translation has solidified itself as a key focus of artificial intelligence research today. However,…
Efficient Large Scale Language Modeling with Mixtures of Experts
Mikel Artetxe, Shruti Bhosale, Naman Goyal +21
Mixture of Experts layers (MoEs) enable efficient scaling of language models through conditional computation. This paper presents a detailed empirical study of how autoregressive M…
Few-shot Learning with Multilingual Language Models
Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe +18
Large-scale generative language models such as GPT-3 are competitive few-shot learners. While these models are known to be able to jointly represent many different languages, their…