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cs.CL2024
Can We Afford The Perfect Prompt? Balancing Cost and Accuracy with the Economical Prompting Index
Tyler McDonald, Anthony Colosimo, Yifeng Li +1
As prompt engineering research rapidly evolves, evaluations beyond accuracy are crucial for developing cost-effective techniques. We present the Economical Prompting Index (EPI), a…
cs.CL2024
NYT-Connections: A Deceptively Simple Text Classification Task that Stumps System-1 Thinkers
Angel Yahir Loredo Lopez, Tyler McDonald, Ali Emami
Large Language Models (LLMs) have shown impressive performance on various benchmarks, yet their ability to engage in deliberate reasoning remains questionable. We present NYT-Conne…
cs.CL2024
STOP! Benchmarking Large Language Models with Sensitivity Testing on Offensive Progressions
Robert Morabito, Sangmitra Madhusudan, Tyler McDonald +1
Mitigating explicit and implicit biases in Large Language Models (LLMs) has become a critical focus in the field of natural language processing. However, many current methodologies…