We aim to use large language models (LLMs) for multi-features time series forecasting in stock prediction, where we leverage multiple alphas (domainspecific time series features) and their explanations (text fe
The recent advancements in large language models (LLMs) have revolutionized the field of natural language processing, progressively broadening their scope to multimodal perception and generation. However, effectively integrating listening capabilities into LLMs poses significant challenges, particul...
Occupational Bias in Open-Source Pretrained Large Language Models: Analyzing Polarity towards Creative and Technical Professions - Ph1n-Pham/bias-in-llms
This paper presents a comprehensive survey of ChatGPT-related (GPT-3.5 and GPT-4) research, state-of-the-art large language models (LLM) from the GPT series, and their prospective applications across diverse domains. Indeed, key innovations such as large-scale pre-training that captures knowledg...
Black-Box Tuning (BBT) is a gradient-free method to drive large language models (LLMs) for few-shot learning. It optimizes a sequence of soft prompt tokens prepended to the input of LLMs, without requiring gradients/back-propagation of the LLMs. Therefore, pre-trained general-purposed LLM...
urban decision support system; large language models; ontology; intermodal freight transportation; artificial intelligence1. Introduction The global urban population has surged from 1.01 billion in 1960 to 4.52 billion in 2022 and is expected to reach 6.9 billion, or approximately 70% of the global ...
Towards Optimizing Multi-Level Selective Maintenance via Machine Learning Predictive ModelsMACHINE learningFLEXIBLE manufacturing systemsPLANT maintenanceMETAHEURISTIC algorithmsPREDICTIVE control systemsPREDICTION modelsThe maintenance strategies commonly employed in industrial settings primarily rely on theoretical models ...
Our Columns Data Science Columns on TDS are carefully curated collections of posts on a particular idea or category… TDS Editors November 14, 2020 4 min read Optimizing Marketing Campaigns with Budgeted Multi-Armed Bandits Data Science With demos, our new solution, and a video ...
Challenge 3: Optimizing network protocols to connect all things Today, our primary networks can support tens of billions of consumer connections. By 2030, they will need to support trillions of industry connections. This will bring three major obstacles to network protocols. ...
To understand whether sycophancy in preference data is responsible for sycophancy in AI assistants, we then analyze whether sycophancy increases when optimizing language model responses using preference models (PMs) that are trained in part on human preference judgements. Specifically, we optimize respon...