18 citations · 36 across the 10 of their papers we have counts for
10 papers
Evaluation of ML Resource Utilization Requires Model Life Cycle Assessment
Jared Fernandez, Clara Na, Yonatan Bisk +2
Proper accounting of the energy requirements and environmental impact of artificial intelligence (AI) systems is necessary for researchers, developers, policy makers, and users to…
BlueFin: Benchmarking LLM Agents on Financial Spreadsheets
Srivatsa Kundurthy, Clara Na, Colton Moraine +6
We present BlueFin, a benchmark that tasks large language model (LLM) agents with synthesis, manipulation, and comprehension tasks over spreadsheet workbooks in the professional fi…
SpreadsheetArena: Decomposing Preference in LLM Generation of Spreadsheet Workbooks
Srivatsa Kundurthy, Clara Na, Michael Handley +5
We consider the task of end-to-end spreadsheet generation, where language models produce spreadsheet artifacts to satisfy users' explicit and implicit constraints, specified in nat…
Energy Considerations of Large Language Model Inference and Efficiency Optimizations
Jared Fernandez, Clara Na, Vashisth Tiwari +3
As large language models (LLMs) scale in size and adoption, their computational and environmental costs continue to rise. Prior benchmarking efforts have primarily focused on laten…
Holistically Evaluating the Environmental Impact of Creating Language Models
Jacob Morrison, Clara Na, Jared Fernandez +3
As the performance of artificial intelligence systems has dramatically increased, so too has the environmental impact of creating these systems. While many model developers release…
Scalable Data Ablation Approximations for Language Models through Modular Training and Merging
Clara Na, Ian Magnusson, Ananya Harsh Jha +4
Training data compositions for Large Language Models (LLMs) can significantly affect their downstream performance. However, a thorough data ablation study exploring large sets of c…
Energy and Carbon Considerations of Fine-Tuning BERT
Xiaorong Wang, Clara Na, Emma Strubell +2
Despite the popularity of the `pre-train then fine-tune' paradigm in the NLP community, existing work quantifying energy costs and associated carbon emissions has largely focused o…
To Build Our Future, We Must Know Our Past: Contextualizing Paradigm Shifts in Natural Language Processing
Sireesh Gururaja, Amanda Bertsch, Clara Na +2
NLP is in a period of disruptive change that is impacting our methodologies, funding sources, and public perception. In this work, we seek to understand how to shape our future by…
The Framework Tax: Disparities Between Inference Efficiency in NLP Research and Deployment
Jared Fernandez, Jacob Kahn, Clara Na +2
Increased focus on the computational efficiency of NLP systems has motivated the design of efficient model architectures and improvements to underlying hardware accelerators. Howev…
Train Flat, Then Compress: Sharpness-Aware Minimization Learns More Compressible Models
Clara Na, Sanket Vaibhav Mehta, Emma Strubell
Model compression by way of parameter pruning, quantization, or distillation has recently gained popularity as an approach for reducing the computational requirements of modern dee…