11 citations · 17 across the 10 of their papers we have counts for
10 papers
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…
CrowdMath: A Dataset of Crowdsourced Mathematical Research Discussions
Sherin Muckatira, Jesse Geneson, Slava Gerovitch +3
Large language models have made substantial progress on mathematical reasoning, but existing benchmarks typically evaluate well-specified problems with final answers, step-by-step…
A Pre-Training Analogue of Grokking in Language Models: Tracing Delayed Grammatical Generalization
Sherin Muckatira, Namrata Shivagunde, Vijeta Deshpande +1
Grokking, the phenomenon in which neural networks generalize long after fitting their training data, has been studied in supervised settings on many epochs. LLM pre-training instea…
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…
Beyond Perplexity: A Geometric and Spectral Study of Low-Rank Pre-Training
Namrata Shivagunde, Vijeta Deshpande, Sherin Muckatira +1
Pre-training large language models is dominated by the memory cost of storing full-rank weights, gradients, and optimizer states. Low-rank pre-training has emerged to address this,…
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…
Emergent Abilities in Reduced-Scale Generative Language Models
Sherin Muckatira, Vijeta Deshpande, Vladislav Lialin +1
Large language models can solve new tasks without task-specific fine-tuning. This ability, also known as in-context learning (ICL), is considered an emergent ability and is primari…
Let's Reinforce Step by Step
Sarah Pan, Vladislav Lialin, Sherin Muckatira +1
While recent advances have boosted LM proficiency in linguistic benchmarks, LMs consistently struggle to reason correctly on complex tasks like mathematics. We turn to Reinforcemen…
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…
Properties Of Winning Tickets On Skin Lesion Classification
Sherin Muckatira
Skin cancer affects a large population every year -- automated skin cancer detection algorithms can thus greatly help clinicians. Prior efforts involving deep learning models have…