most citedLIMA: Less Is More for Alignment

128 citations · 212 across the 5 of their papers we have counts for

collaborators

5 papers

cs.IR20231 cited

DataFinder: Scientific Dataset Recommendation from Natural Language Descriptions

Vijay Viswanathan, Luyu Gao, Tongshuang Wu +2

Modern machine learning relies on datasets to develop and validate research ideas. Given the growth of publicly available data, finding the right dataset to use is increasingly dif…

cs.CL20232 cited

GlobalBench: A Benchmark for Global Progress in Natural Language Processing

Yueqi Song, Catherine Cui, Simran Khanuja +9

Despite the major advances in NLP, significant disparities in NLP system performance across languages still exist. Arguably, these are due to uneven resource allocation and sub-opt…

cs.CL2023128 cited

LIMA: Less Is More for Alignment

Chunting Zhou, Pengfei Liu, Puxin Xu +12

Large language models are trained in two stages: (1) unsupervised pretraining from raw text, to learn general-purpose representations, and (2) large scale instruction tuning and re…

cs.CL202381 cited

GPTScore: Evaluate as You Desire

Jinlan Fu, See-Kiong Ng, Zhengbao Jiang +1

Generative Artificial Intelligence (AI) has enabled the development of sophisticated models that are capable of producing high-caliber text, images, and other outputs through the u…

cs.AI2022

KGxBoard: Explainable and Interactive Leaderboard for Evaluation of Knowledge Graph Completion Models

Haris Widjaja, Kiril Gashteovski, Wiem Ben Rim +5

Knowledge Graphs (KGs) store information in the form of (head, predicate, tail)-triples. To augment KGs with new knowledge, researchers proposed models for KG Completion (KGC) task…