activity
20162024
most citedThe Flan Collection: Designing Data and Methods for Effective Instruction Tuning

113 citations · 296 across the 14 of their papers we have counts for

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Showing cs.CLShow all

8 papers · 1 filter

cs.CL20246 cited

Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation

Shivalika Singh, Angelika Romanou, Clémentine Fourrier +21

Cultural biases in multilingual datasets pose significant challenges for their effectiveness as global benchmarks. These biases stem not only from differences in language but also…

cs.CL202413 cited

Consent in Crisis: The Rapid Decline of the AI Data Commons

Shayne Longpre, Robert Mahari, Ariel Lee +46

General-purpose artificial intelligence (AI) systems are built on massive swathes of public web data, assembled into corpora such as C4, RefinedWeb, and Dolma. To our knowledge, we…

cs.CL20249 cited

Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model

Ahmet Üstün, Viraat Aryabumi, Zheng-Xin Yong +14

Recent breakthroughs in large language models (LLMs) have centered around a handful of data-rich languages. What does it take to broaden access to breakthroughs beyond first-class…

cs.CL20236 cited

The Data Provenance Initiative: A Large Scale Audit of Dataset Licensing & Attribution in AI

Shayne Longpre, Robert Mahari, Anthony Chen +14

The race to train language models on vast, diverse, and inconsistently documented datasets has raised pressing concerns about the legal and ethical risks for practitioners. To reme…

cs.CL202365 cited

The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset

Hugo Laurençon, Lucile Saulnier, Thomas Wang +51

As language models grow ever larger, the need for large-scale high-quality text datasets has never been more pressing, especially in multilingual settings. The BigScience workshop,…

cs.CL20224 cited

Combining Compressions for Multiplicative Size Scaling on Natural Language Tasks

Rajiv Movva, Jinhao Lei, Shayne Longpre +2

Quantization, knowledge distillation, and magnitude pruning are among the most popular methods for neural network compression in NLP. Independently, these methods reduce model size…