69 citations · 79 across the 11 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…
A Penalty Goes a Long Way: Measuring Lexical Diversity in Synthetic Texts Under Prompt-Influenced Length Variations
Vijeta Deshpande, Ishita Dasgupta, Uttaran Bhattacharya +3
Synthetic text generated by Large Language Models (LLMs) is increasingly used for further training and improvement of LLMs. Diversity is crucial for the effectiveness of synthetic…
Diverse, not Short: A Length-Controlled Data Selection Strategy for Improving Response Diversity of Language Models
Vijeta Deshpande, Debasmita Ghose, John D. Patterson +2
Diverse language model responses are crucial for creative generation, open-ended tasks, and self-improvement training. We show that common diversity metrics, and even reward models…
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…
Honey, I Shrunk the Language: Language Model Behavior at Reduced Scale
Vijeta Deshpande, Dan Pechi, Shree Thatte +2
In recent years, language models have drastically grown in size, and the abilities of these models have been shown to improve with scale. The majority of recent scaling laws studie…
Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning
Vladislav Lialin, Vijeta Deshpande, Xiaowei Yao +1
This paper presents a systematic overview of parameter-efficient fine-tuning methods, covering over 50 papers published between early 2019 and mid-2024. These methods aim to addres…