11 citations · 13 across the 9 of their papers we have counts for
7 papers · 1 filter
AGC-Bench: Measuring Artificial General Creativity
Roger Beaty, Vijeta Deshpande, Clin K. Y. Lai +9
Creativity research has debated whether creativity is domain-specific (e.g., visual, writing, science), and if it is psychometrically separable from general intelligence. Both ques…
Playing with Words, Improving with Rewards: Training Language Models for Creative Association
Vijeta Deshpande, Namrata Shivagunde, Sherin Muckatira +5
Large Language Models (LLMs) are being applied to increasingly difficult problems and use cases. To navigate their vast solution spaces effectively, LLMs need to be creative. Yet t…
Deconstructing In-Context Learning: Understanding Prompts via Corruption
Namrata Shivagunde, Vladislav Lialin, Sherin Muckatira +1
The ability of large language models (LLMs) to learn in context based on the provided prompt has led to an explosive growth in their use, culminating in the proliferation of…
ReLoRA: High-Rank Training Through Low-Rank Updates
Vladislav Lialin, Namrata Shivagunde, Sherin Muckatira +1
Despite the dominance and effectiveness of scaling, resulting in large networks with hundreds of billions of parameters, the necessity to train overparameterized models remains poo…
Larger Probes Tell a Different Story: Extending Psycholinguistic Datasets Via In-Context Learning
Namrata Shivagunde, Vladislav Lialin, Anna Rumshisky
Language model probing is often used to test specific capabilities of models. However, conclusions from such studies may be limited when the probing benchmarks are small and lack s…
Down and Across: Introducing Crossword-Solving as a New NLP Benchmark
Saurabh Kulshreshtha, Olga Kovaleva, Namrata Shivagunde +1
Solving crossword puzzles requires diverse reasoning capabilities, access to a vast amount of knowledge about language and the world, and the ability to satisfy the constraints imp…
Life after BERT: What do Other Muppets Understand about Language?
Vladislav Lialin, Kevin Zhao, Namrata Shivagunde +1
Existing pre-trained transformer analysis works usually focus only on one or two model families at a time, overlooking the variability of the architecture and pre-training objectiv…