🔍🤖PsychoBench

RESEARCH USE ONLY✅ NO COMMERCIAL USE ALLOWED❌

Benchmarking LLMs' Psychological Portray.

UPDATES

[Jan 16 2024]: PsychoBench is accepted to ICLR 2024 Oral (1.2%)

[Dec 28 2023]: Add support to 16personalities.com

🛠️ Usage

✨An example run:

python run_psychobench.py \
  --model gpt-3.5-turbo \
  --questionnaire EPQ-R \
  --openai-key "<openai_api_key>"\
  --shuffle-count 1 \
  --test-count 2

✨An example result:

Category	gpt-4 (n = 10)	Male (n = 693)	Female (n = 878)
Extraversion	13.9 $\pm$ 4.3	12.5 $\pm$ 6.0	14.1 $\pm$ 5.1
Pschoticism	17.8 $\pm$ 2.1	7.2 $\pm$ 4.6	5.7 $\pm$ 3.9
Neuroticism	3.9 $\pm$ 6.0	10.5 $\pm$ 5.8	12.5 $\pm$ 5.1
Lying	7.0 $\pm$ 2.1	7.1 $\pm$ 4.3	6.9 $\pm$ 4.0

🔧 Argument Specification

1. --questionnaire: (Required) Select the questionnaire(s) to run. For choises please see the list bellow.

2. --model: (Required) The name of the model to test.

3. --shuffle-count: (Required) Numbers of different orders. If set zero, run only the original order. If set n > 0, run the original order along with its n permutations. Defaults to zero.

4. --test-count: (Required) Numbers of runs for a same order. Defaults to one.

5. --name-exp: Name of this run. Is used to name the result files.

6. --significance-level: The significance level for testing the difference of means between human and LLM. Defaults to 0.01.

7. --mode: For debugging. To choose which part of the code is running.

Arguments related to openai API (can be discarded when users customize models):

1. --openai-key: Your API key. Can be found in View API keys -> API keys.

🦙 Benchmarking Your Own Model

It is easy! Just replace the function example_generator fed into the function run_psychobench(args, generator).

Your customized function your_generator() does the following things:

1. Read questions from the file args.testing_file. The file locates under results/ (check run_psychobench() in utils.py) and has the following format:

Prompt: ...	order-1	shuffle0-test0	shuffle0-test1	Prompt: ...	order-2	shuffle0-test0	shuffle0-test1
Q1	1			Q3	3
Q2	2			Q5	5
...	...			...	...
Qn	n			Q1	1

You can read the columns before each column starting with order-, which contains the shuffled questions for your input.

2. Call your own LLM and get the results.

3. Fill in the blank in the file args.testing_file. Remember: No need to map the response to its original order. Our code will take care of it.

Please check example_generator.py for datailed information.

📃 Questionnaire List (Choices for Argument: --questionnaire)

To include multiple questionnaires, use a comma to separate them. For example: --questionnaire BFI,DTDD,EPQ-R.

To include ALL questionnaires, just use --questionnaire ALL.

1. Big Five Inventory: --questionnaire BFI

2. Dark Triad Dirty Dozen: --questionnaire DTDD

3. Eysenck Personality Questionnaire-Revised: --questionnaire EPQ-R

4. Experiences in Close Relationships-Revised (Adult Attachment Questionnaire): --questionnaire ECR-R

5. Comprehensive Assessment of Basic Interests: --questionnaire CABIN

6. General Self-Efficacy: --questionnaire GSE

7. Love of Money Scale: --questionnaire LMS

8. Bem's Sex Role Inventory: --questionnaire BSRI

9. Implicit Culture Belief: --questionnaire ICB

10. Revised Life Orientation Test: --questionnaire LOT-R

11. Empathy Scale: --questionnaire Empathy

12. Emotional Intelligence Scale: --questionnaire EIS

13. Wong and Law Emotional Intelligence Scale: --questionnaire WLEIS

👉 Paper and Citation

For more details, please refer to our paper here.

If you find our paper&tool interesting and useful, please feel free to give us a star and cite us through:

@inproceedings{huang2024humanity,
  author    = {Jen{-}tse Huang and
               Wenxuan Wang and
               Eric John Li and
               Man Ho Lam and
               Shujie Ren and
               Youliang Yuan and
               Wenxiang Jiao and
               Zhaopeng Tu and
               Michael R. Lyu},
  title     = {On the Humanity of Conversational AI: Evaluating the Psychological Portrayal of LLMs},
  booktitle = {Proceedings of the Twelfth International Conference on Learning Representations (ICLR)},
  year      = {2024}
}