Zobrazeno 1 - 10
of 47
pro vyhledávání: '"Foulds, James R"'
Large language models (LLMs) like ChatGPT demonstrate the remarkable progress of artificial intelligence. However, their tendency to hallucinate -- generate plausible but false information -- poses a significant challenge. This issue is critical, as
Externí odkaz:
http://arxiv.org/abs/2403.01193
Publikováno v:
Workshops at the International Conference on Intelligent User Interfaces (IUI) 2024
The accelerating advancements in Artificial Intelligence (AI) and Machine Learning (ML), highlighted by the development of cutting-edge Generative Pre-trained Transformer (GPT) models by organizations such as OpenAI, Meta, and Anthropic, present new
Externí odkaz:
http://arxiv.org/abs/2402.01663
Recent advances in large language models (LLMs), such as ChatGPT, have led to highly sophisticated conversation agents. However, these models suffer from "hallucinations," where the model generates false or fabricated information. Addressing this cha
Externí odkaz:
http://arxiv.org/abs/2306.06085
We have developed a set of Python applications that use large language models to identify and analyze data from social media platforms relevant to a population of interest. Our pipeline begins with using OpenAI's GPT-3 to generate potential keywords
Externí odkaz:
http://arxiv.org/abs/2301.05198
It is now well understood that machine learning models, trained on data without due care, often exhibit unfair and discriminatory behavior against certain populations. Traditional algorithmic fairness research has mainly focused on supervised learnin
Externí odkaz:
http://arxiv.org/abs/2209.07044
Text analysis of social media for sentiment, topic analysis, and other analysis depends initially on the selection of keywords and phrases that will be used to create the research corpora. However, keywords that researchers choose may occur infrequen
Externí odkaz:
http://arxiv.org/abs/2204.07483
User-generated data on social media contain rich information about who we are, what we like and how we make decisions. In this paper, we survey representative work on learning a concise latent user representation (a.k.a. user embedding) that can capt
Externí odkaz:
http://arxiv.org/abs/2105.07996
This paper describes a method for using Transformer-based Language Models (TLMs) to understand public opinion from social media posts. In this approach, we train a set of GPT models on several COVID-19 tweet corpora that reflect populations of users
Externí odkaz:
http://arxiv.org/abs/2104.10259
Healthcare programs such as Medicaid provide crucial services to vulnerable populations, but due to limited resources, many of the individuals who need these services the most languish on waiting lists. Survival models, e.g. the Cox proportional haza
Externí odkaz:
http://arxiv.org/abs/2010.06820
Word embedding models such as the skip-gram learn vector representations of words' semantic relationships, and document embedding models learn similar representations for documents. On the other hand, topic models provide latent representations of th
Externí odkaz:
http://arxiv.org/abs/1909.04702