Prompt Engineering Benchmark¶
The Prompt Engineering Module collects a variety of prompting methods and evaluates their performance across multiple datasets. This module currently supports models including GPT-3.5-turbo and GPT-4-1106.
Please contact us if you want the results of your models shown in this leaderboard.
All Results¶
GPT3.5 -Turbo |
GPT4-1106 |
|||||||
|---|---|---|---|---|---|---|---|---|
gsm8k |
bigbench_date |
bigbench_object_tracking |
csqa |
gsm8k |
bigbench_date |
bigbench_object_tracking |
csqa |
|
baseline |
47.15 |
57.99 |
39.2 |
72.48 |
92.19 |
87.80 |
96.27 |
79.69 |
CoT |
40.33 |
49.32 |
63.20 |
67.81 |
85.89 |
92.14 |
90.26 |
85.59 |
CoT(zero-shot) |
18.50 |
80.49 |
66.00 |
65.85 |
87.34 |
87.53 |
99.07 |
79.85 |
expert prompting |
21.15 |
61.79 |
56.53 |
74.45 |
88.70 |
87.26 |
98.93 |
79.85 |
emotion prompt |
57.24 |
66.12 |
29.87 |
70.68 |
90.83 |
87.80 |
95.73 |
80.34 |
GPT3.5-Turbo |
GPT4-1106 |
|||
|---|---|---|---|---|
gsm8k |
last-letter-concat |
gsm8k |
last-letter-concat |
|
baseline |
47.15 |
7.2 |
92.19 |
25.2 |
least to most |
75.28 |
79.8 |
79.38 |
96.2 |
“This is very important to my career.” is used in emotion prompt