Identifying Reasons for Contraceptive Switching from Real-World Data Using Large Language Models
University of California San Francisco（加州大学旧金山分校）；Brigham and Women’s Hospital（布列根和妇女医院）；Harvard Medical School（哈佛医学院）；University of California（加利福尼亚大学）；University of California, Berkeley（加州大学伯克利分校）
Brenda Y. Miao, Christopher YK Williams, Ebenezer Chinedu-Eneh, Travis Zack, Emily Alsentzer, Atul J. Butte, Irene Y. Chen
Prescription contraceptives play a critical role in supporting women's reproductive health. With nearly 50 million women in the United States using contraceptives, understanding the factors that drive contraceptives selection and switching is of significant interest. However, many factors related to medication switching are often only captured in unstructured clinical notes and can be difficult to extract. Here, we evaluate the zero-shot abilities of a recently developed large language model, GPT-4 (via HIPAA-compliant Microsoft Azure API), to identify reasons for switching between classes of contraceptives from the UCSF Information Commons clinical notes dataset. We demonstrate that GPT-4 can accurately extract reasons for contraceptive switching, outperforming baseline BERT-based models with microF1 scores of 0.849 and 0.881 for contraceptive start and stop extraction, respectively. Human evaluation of GPT-4-extracted reasons for switching showed 91.4% accuracy, with minimal hallucinations. Using extracted reasons, we identified patient preference, adverse events, and insurance as key reasons for switching using unsupervised topic modeling approaches. Notably, we also showed using our approach that "weight gain/mood change" and "insurance coverage" are disproportionately found as reasons for contraceptive switching in specific demographic populations. Our code and supplemental data are available at https://github.com/BMiao10/contraceptive-switching.
Balancing Efficiency and Efficacy: Training-Free Attention-Guided Switching Between Explicit and Latent Thoughts for MLLMs
Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Peng Cheng Laboratory（鹏城实验室）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
Can Large Language Models Understand, Reason About, and Generate Code-Switched Text?
Capital One（第一资本）；Stanford University（斯坦福大学）；MBZUAI（穆罕默德·本·扎耶德人工智能大学）；KAIST（韩国科学技术院）；Brown University（布朗大学）；NAIST（奈良先端科学技术大学院大学）；UC Berkeley（加州大学伯克利分校）；SAS Posos；Oracle（甲骨文公司）；ITB（万隆理工学院）；The Hong Kong Polytechnic University（香港理工大学）
AdaSwitch: Adaptive Switching between Small and Large Agents for Effective Cloud-Local Collaborative Learning
State Key Laboratory of General Artificial Intelligence, Peking University, Beijing, China（一般人工智能国家重点实验室，北京大学，北京，中国）；School of Intelligence Science and Technology, Peking University（智能科学与技术学院，北京大学）；East China Normal University（东华大学）；Chinese Academy of Sciences（中国科学院）；Baidu Inc（百度公司）；Beijing Institute of Technology（北京理工大学）；University of Birmingham（伯明翰大学）
SwitchCIT: Switching for Continual Instruction Tuning
Coordinated Science Laboratory（协调科学实验室）；Department of Electrical and Computer Engineering（电气与计算机工程系）；Department of Mathematics（数学系）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
The Role of Mixed-Language Documents for Multilingual Large Language Model Pretraining
University College London（伦敦大学学院）；Nanyang Technological University（南洋理工大学）；University of Waterloo（滑铁卢大学）；NVIDIA（英伟达）；National Institute of Informatics（信息研究院）
A Learning Rate Path Switching Training Paradigm for Version Updates of Large Language Models
School of Informatics, Xiamen University（厦门大学信息学院）；vivo AI Lab（vivo人工智能实验室）；Institute of Artificial Intelligence, Xiamen University（厦门大学人工智能研究院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
Blind Spot Navigation in Large Language Model Reasoning with Thought Space Explorer
Clemson University（克莱姆森大学）；Université de Montréal（蒙特利尔大学）；Center for Advanced AI, Accenture（Accenture高级人工智能中心）；The University of Alabama（阿拉巴马大学）；University of Kansas（堪萨斯大学）；Arizona State University（亚利桑那州立大学）
The Harker School（哈克学校）；University of California, Santa Cruz（加州大学圣克鲁兹分校）
Code-switching, or switching between languages, occurs for many reasons and has important linguistic, sociological, and cultural implications. Multilingual speakers code-switch for a variety of purposes, such as expressing emotions, borrowing terms, making jokes, introducing a new topic, etc. The reason for code-switching may be quite useful for analysis, but is not readily apparent. To remedy this situation, we annotate a new dataset of motivations for code-switching in Spanish-English. We build the first system (to our knowledge) to automatically identify a wide range of motivations that spe…
IIIT Sri City（印度信息技术学院斯里城）；UCLA（加州大学洛杉矶分校）；Wipro AI Labs（威普罗人工智能实验室）；AI Institute, University of South Carolina（南卡罗来纳大学人工智能研究所）
NLP applications for code-mixed (CM) or mix-lingual text have gained a significant momentum recently, the main reason being the prevalence of language mixing in social media communications in multi-lingual societies like India, Mexico, Europe, parts of USA etc. Word embeddings are basic build-ing blocks of any NLP system today, yet, word embedding for CM languages is an unexplored territory. The major bottleneck for CM word embeddings is switching points, where the language switches. These locations lack in contextually and statistical systems fail to model this phenomena due to high variance …
Adapting Language Balance in Code-Switching Speech
Interactive Systems Lab, Karlsruhe Institut of Technology (KIT), Germany（互动系统实验室，卡尔斯鲁厄理工学院（KIT），德国）；InterACT, Carnegie Mellon University (CMU), USA（InterACT，卡内基梅隆大学（CMU），美国）
Leveraging user-provided translation to constrain NMT has practical significance. Existing methods can be classified into two main categories, namely the use of placeholder tags for lexicon words and the use of hard constraints during decoding. Both methods can hurt translation fidelity for various reasons. We investigate a data augmentation method, making code-switched training data by replacing source phrases with their target translations. Our method does not change the MNT model or decoding algorithm, allowing the model to learn lexicon translations by copying source-side target words. Ext…
Bar-Ilan University（巴伊兰大学）；Allen Institute for Artificial Intelligence（艾伦人工智能研究所）
We focus on the problem of language modeling for code-switched language, in the context of automatic speech recognition (ASR). Language modeling for code-switched language is challenging for (at least) three reasons: (1) lack of available large-scale code-switched data for training; (2) lack of a replicable evaluation setup that is ASR directed yet isolates language modeling performance from the other intricacies of the ASR system; and (3) the reliance on generative modeling. We tackle these three issues: we propose an ASR-motivated evaluation setup which is decoupled from an ASR system and th…
Code-switching (CS), a ubiquitous phenomenon due to the ease of communication it offers in multilingual communities still remains an understudied problem in language processing. The primary reasons behind this are: (1) minimal efforts in leveraging large pretrained multilingual models, and (2) the lack of annotated data. The distinguishing case of low performance of multilingual models in CS is the intra-sentence mixing of languages leading to switch points. We first benchmark two sequence labeling tasks -- POS and NER on 4 different language pairs with a suite of pretrained models to identify…
One Patient, Many Contexts: Scaling Medical AI with Contextual Intelligence
Harvard Medical School（哈佛医学院）；Clalit Research Institute（Clalit研究所）；Boston Children’s Hospital（波士顿儿童医院）；Harvard Data Science Initiative（哈佛数据科学计划）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；Harvard T.H. Chan School of Public Health（哈佛大学陈曾熙公共卫生学院）；Clalit Health Services（Clalit医疗服务集团）；Ben Gurion University（本-古里安大学）；Ben Gurion University of the Negev（内盖夫本-古里安大学）；Brigham and Women’s Hospital（布莱根妇女医院）；Harvard University（哈佛大学）；Broad Institute of MIT and Harvard（麻省理工学院与哈佛大学布罗德研究所）
