Sercan Arık
767 posts
@sercanarik
Artificial Intelligence @Google. Former Stanford PhD. Cultural, interdisciplinary enthusiast. I tweet about things that interest me.
Introducing ASPIRE, a framework that enhances the selective prediction capabilities of large language models, enabling them to output an answer paired with a confidence score. Learn how it outperforms state-of-the-art methods on a variety of QA datasets. → goo.gle/3RWpChi
Introducing a new approach for adaptive prompting of #LLMs that train with unlabeled samples + pseudo-demonstrations generated by the model itself to close the gap between few-shot and 0-shot performance on reasoning, NLU and language generation tasks. → goo.gle/3tY00IB
Introducing TSMixer, an advanced multivariate model for long-term forecasting that leverages linear model characteristics for high benchmark performance. Learn how it outperforms state-of-the-art models when evaluated on real-world applications → goo.gle/3PulkOj
State-of-the-art Text-to-SQL with SQL-PaLM - proposes SQL-PaLM, an LLM-based Text-to-SQL adopted from PaLM-2 - achieves SoTA in both in-context learning and fine-tuning settings - incredible that the few-shot model outperforms the previous fine-tuned SoTA by 3.8% on the Spider benchmark - few-shot SQL-PaLM also outperforms few-shot GPT-4 by 9.9%, using a simple prompting approach arxiv.org/abs/2306.00739
Analyzing electronic health records (EHRs) has great potential, e.g. to enhance patient care, but common anonymization methods can decrease the data’s utility. To that end, read how EHR-Safe generates high-fidelity & privacy-preserving synthetic EHR data→ goo.gle/3HZfpxn
The Visual Transformer has helped advance many core computer vision applications, e.g., image classification, but training can be inefficient and models lack interpretable designs. Learn how the Nested Hierarchical Transformer addresses these challenges → goo.gle/3sAy7Ca
Introducing a novel approach for interpretable, robust, and reliable deep neural networks (DNNs) that employs controllable rule representations, which do not require retraining to adjust the rule strength at inference. Learn more below ↓ goo.gle/3IN8TGU
Announcing the Temporal Fusion Transformer, designed specifically to handle the heterogeneity of data in multi-horizon forecasting, which achieves more accurate forecasts with increased interpretability. Read more, including real-world applications ↓ goo.gle/3oPgE84
Learn more about a new ML-based framework for epidemiology that we applied to COVID-19, including forecasts that are released to the public daily. Read all about how it was developed and has been used by large organizations ↓ goo.gle/3Azo2Hj
