Are Large Language Models Capable of Causal Reasoning for Sensing Data Analysis?

Author:

Hu Zhizhang1ORCID,Zhang Yue1ORCID,Rossi Ryan2ORCID,Yu Tong2ORCID,Kim Sungchul2ORCID,Pan Shijia1ORCID

Affiliation:

1. University of California, Merced

2. Adobe Research

Funder

UC Merced Spring 2023 Climate Action Seed Competition Grant

Publisher

ACM

Reference20 articles.

1. Air Pollution, Socioeconomic Status, and Age-Specific Mortality Risk in the United States

2. Tom Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared D Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell et al. 2020. Language models are few-shot learners. Advances in neural information processing systems 33 (2020) 1877--1901.

3. Wenhu Chen, Xueguang Ma, Xinyi Wang, and William W Cohen. 2022. Program of thoughts prompting: Disentangling computation from reasoning for numerical reasoning tasks. arXiv preprint arXiv:2211.12588 (2022).

4. TheoremQA: A Theorem-driven Question Answering Dataset

5. Poster Abstract: Enhancing Fault Resilience of Air Quality Monitoring in San Joaquin Valley: A Data Equity Analysis

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