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Scaling Laws Are Unreliable for Downstream Tasks: A Reality Check
Nicholas Lourie, Michael Y. Hu, Kyunghyun Cho
Downstream scaling laws aim to predict task performance at larger scales from the model's performance at smaller scales. Whether such prediction should be possible is unclear: some…
What's In Your Field? Mapping Scientific Research with Knowledge Graphs and Large Language Models
Abhipsha Das, Nicholas Lourie, Siavash Golkar +1
The scientific literature's exponential growth makes it increasingly challenging to navigate and synthesize knowledge across disciplines. Large language models (LLMs) are powerful…
Show Your Work with Confidence: Confidence Bands for Tuning Curves
Nicholas Lourie, Kyunghyun Cho, He He
The choice of hyperparameters greatly impacts performance in natural language processing. Often, it is hard to tell if a method is better than another or just better tuned. Tuning…
UNICORN on RAINBOW: A Universal Commonsense Reasoning Model on a New Multitask Benchmark
Nicholas Lourie, Ronan Le Bras, Chandra Bhagavatula +1
Commonsense AI has long been seen as a near impossible goal -- until recently. Now, research interest has sharply increased with an influx of new benchmarks and models. We propose…
Learning from Task Descriptions
Orion Weller, Nicholas Lourie, Matt Gardner +1
Typically, machine learning systems solve new tasks by training on thousands of examples. In contrast, humans can solve new tasks by reading some instructions, with perhaps an exam…
Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics
Swabha Swayamdipta, Roy Schwartz, Nicholas Lourie +4
Large datasets have become commonplace in NLP research. However, the increased emphasis on data quantity has made it challenging to assess the quality of data. We introduce Data Ma…