GPT-4 results in decreased error rate for all tasks with increasing complexity when compared to GPT-3.5-turbo. by Jacqueline A. Jansen (14894276)

The attraction force generated by the attractor set on the robot. by Long Di (9977453)

Parameter sets generated through Latin hypercube sampling. by Alexandra M. Campbell (3284094)

Data Sheet 1_Emotional prompting amplifies disinformation generation in AI large language models.docx by Rasita Vinay (21006911)

“…Introduction<p>The emergence of artificial intelligence (AI) large language models (LLMs), which can produce text that closely resembles human-written content, presents both opportunities and risks. …”

Feasibility of AI-powered assessment scoring: Can large language models replace human raters? by Michael Jaworski III (22156096)

Raw data files for IMC data sets. by Kafayat Yusuf (22104155)

Classification model parameter settings. by Chao Tang (10925)

Parameter setting table. by Bo Tong (2138632)

PCA-CGAN model parameter settings. by Chao Tang (10925)

Minimal data set. by Jingru Zhou (766195)

Summary of codes generated from MAXQDA software. by Awole Seid (3839965)

Data Sheet 1_Large language models for closed-library multi-document query, test generation, and evaluation.docx by Claire Randolph (19747105)

“…</p>Results<p>Coverage of source documents by LLM-RAG generated questions decreases as the length of documents increase. …”

Generated spline library. by Zhe Hu (787283)

“…Then, the LSTM model is trained using this sliding window approach, achieving a root mean square error(RMSE) of 0.03 on the test set. Field experiments demonstrate that the predicted values from the LSTM model closely align with the measured values, maintaining short-term shape error prediction accuracy within 3 mm. …”

Data set used for analysis. by Florian Simon (5387228)

ActAs and CoT prompt strategies do not result in decreased error rate for tasks with increasing complexity. by Jacqueline A. Jansen (14894276)

Self-correction prompt strategy results in decreased error rate for all tasks with increasing complexity. by Jacqueline A. Jansen (14894276)

fileCont prompt strategy results in decreased error rate for some tasks with increasing complexity. by Jacqueline A. Jansen (14894276)

Model Set Test for Prais–Winsten AR(1) Regression. by Muhummad Khairul Islam (21955544)

Correlation between each MSTM and each generative parameter. by Fabiano Baroni (234173)

Presentation 1_Tracking priming-induced language recovery in aphasia with pre-trained language models.zip by Yan Cong (1491478)