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publications
ProxyLM: Predicting Language Model Performance on Multilingual Tasks via Proxy Models
David Anugraha, Genta Indra Winata, Chenyue Li, Patrick Amadeus Irawan, En-Shiun Annie Lee
Published in TBD, 2024
Performance prediction is a method to estimate the performance of Language Models (LMs) on various Natural Language Processing (NLP) tasks, mitigating computational costs associated with model capacity and data for fine-tuning. Our paper introduces ProxyLM, a scalable framework for predicting LM performance using proxy models in multilingual tasks. These proxy models act as surrogates, approximating the performance of the LM of interest. By leveraging proxy models, ProxyLM significantly reduces computational overhead on task evaluations, achieving up to a 37.08x speedup compared to traditional methods, even with our smallest proxy models. Additionally, our methodology showcases adaptability to previously unseen languages in pre-trained LMs, outperforming the state-of-the-art performance by 1.89x as measured by root-mean-square error (RMSE). This framework streamlines model selection, enabling efficient deployment and iterative LM enhancements without extensive computational resources.
On the Opportunities of (Re)-Exploring Atmospheric Science by Foundation Models: A Case Study
Lujia Zhang*, Hanzhe Cui*, Yurong Song*, Chenyue Li*, Binhang Yuan, Mengqian Lu
Published in arXiv, 2024
Most state-of-the-art AI applications in atmospheric science are based on classic deep learning approaches. However, such approaches cannot automatically integrate multiple complicated procedures to construct an intelligent agent, since each functionality is enabled by a separate model learned from independent climate datasets. The emergence of foundation models, especially multimodal foundation models, with their ability to process heterogeneous input data and execute complex tasks, offers a substantial opportunity to overcome this challenge. In this report, we want to explore a central question - how the state-of-the-art foundation model, i.e., GPT-4o, performs various atmospheric scientific tasks. Toward this end, we conduct a case study by categorizing the tasks into four main classes, including climate data processing, physical diagnosis, forecast and prediction, and adaptation and mitigation. For each task, we comprehensively evaluate the GPT-4o’s performance along with a concrete discussion. We hope that this report may shed new light on future AI applications and research in atmospheric science.
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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