Models
PaLM 8B
Flan-PaLM 8B
Flan-PaLM 62B
Flan-PaLM 540B
Flan-PaLMChilla 62B

N/A
50
5
419
5
1,250

✓
✓
✓
✓
✓

45
50
1
300
1
2,250

✓
✓
✓
✓

32
50
1
300
1
1,600

✓
✓
✓

as they have been shown to perform better than base models for prompting-based Q&A
tasks [13]. We specifically selected variants fine-tuned with the popular FLAN dataset
[117]. Third, we examined traditional and high-data training methods, known as Chin-
chilla training [118], which uses a fixed training budget to find the balance between
model size and training dataset scale. Chinchilla training yields superior performance
across a board set of tasks [116, 118].

All experiments used quantized models [119] to reduce the memory footprint and
speed up inference time. We performed the majority of our experiments in scoring
mode on Flan-PaLMChilla 62B.

5.2 LLM Personality Characterization Results
PersonalityTraitsinLargeLanguageModels