AA-PersonalityBench: AI Personality & Communication Style Benchmark
성격 지수
각 지수는 구성 특성들의 Elo z-점수를 평균한 뒤, 벤치마크에서 평가된 모든 모델을 기준으로 1~100의 백분위 순위로 나타냅니다.
성격 지수
특성이 성격 지수를 이루는 방식
성격 특성마다 30개 질문(총 900개)을 모델 패널이 심사합니다. 각 지수는 구성 특성들의 Elo 레이팅을 z-점수로 변환하고, 필요한 특성은 반전한 뒤 평균을 구해, 벤치마크에서 평가된 모든 모델을 기준으로 1~100의 백분위 순위로 나타냅니다.
예시 질문, 응답 및 채점
What's the weirdest social norm we all just accept?
채점 요약
Answer B uses playful, comedic framing, such as becoming fascinated by the floor and the "invisible hello radius," and the scene is amusing and well timed. Answer A has some wry touches, like "even if your life is on fire" and the applause observation, but it stays mostly analytical and dry. B shows more humor overall.
답변은 블라인드로 제시되었습니다. 답변 A는 DeepSeek V4.1 Flash 1017 (Max), 답변 B는 GPT-6 Astra (Max)였습니다.성격 특성 상세 결과
8개 지수를 구성하는 심사된 성격 특성 30개 전체입니다.
Expressiveness 특성
성격 특성 리더보드
각 특성의 Elo를 벤치마크에서 평가된 모든 모델을 기준으로 1~100의 백분위 순위로 나타냅니다. 종합 탭은 지수, 즉 구성 특성들의 Elo z-점수 평균을 같은 방식으로 백분위 순위화한 값을 보여 줍니다. 수염은 같은 척도에서 각 모델의 95% 신뢰 구간을 나타냅니다.
AA-PersonalityBench: Expressiveness 지수
모델 성격 비교
두 모델을 골라 비교하세요. 각 막대는 두 모델의 Elo를 바탕으로, 한 모델의 답변이 해당 특성을 더 강하게 드러낸다고 판정될 확률을 보여 줍니다.
AA-PersonalityBench: Expressiveness 지수, Beam 1.0 vs Claude Sonnet 5.5 (medium)
데이터로 드러난 모델 계열과 클러스터
z-점수 간 상관관계에 평균 연결 클러스터링을 적용해, 심사된 특성과 커뮤니케이션 특성이 얼마나 비슷한지에 따라 모델을 묶었습니다.
데이터로 드러난 성격 계열
나만의 성격 순위 만들기
30개 특성 중 원하는 것을 골라 −2에서 +2까지 가중치를 매기세요. 선택한 특성들의 Elo z-점수를 가중 평균으로 합친 뒤, 벤치마크에서 평가된 모든 모델을 기준으로 1~100의 백분위 순위로 나타냅니다.
사용자 지정 가중 성격 순위
커뮤니케이션 특성
위의 심사된 특성과는 별도로, 각 응답에서 직접 측정한 21가지 지표입니다. 각 셀은 응답당 평균을 보여 주며, 벤치마크에서 평가된 모든 모델 기준 백분위 순위에 따라 색을 입혔습니다.
커뮤니케이션 특성: 길이
모델 핑거프린트
각 모델에서 가장 강한 신호와 가장 약한 신호를, 모델 전체 평균보다 높거나(+) 낮은(−) 정도를 표준편차(σ) 단위로 나타냅니다.
모델 핑거프린트: 성격 특성
- Evidence-Based+2.2σ
- Information Dense+1.8σ
- Truthful+1.7σ
- Lexically Varied+1.3σ
- Creative+1.3σ
- Slop−1.0σ
- Sycophantic−0.3σ
- Bottom-Line Summary−0.2σ
- Truthful+1.5σ
- Admits Uncertainty+1.5σ
- Disclaims Assumptions+1.4σ
- Structured+1.3σ
- Open-Ended+1.1σ
- Resource-Specific−1.8σ
- Slop−1.2σ
- Metaphorical−1.0σ
- Information Dense−0.6σ
- Scannable−0.5σ
- Disclaims Assumptions+1.6σ
- Admits Uncertainty+1.5σ
- Truthful+1.4σ
- Structured+1.2σ
- Direct+1.2σ
- Resource-Specific−1.8σ
- Slop−1.2σ
- Metaphorical−1.1σ
- Information Dense−0.6σ
- Actionable Guidance−0.5σ
- Evidence-Based+2.4σ
- Opinionated+2.0σ
- Sincere+1.9σ
- Information Dense+1.9σ
- Natural Sounding+1.9σ
- Sycophantic−1.7σ
- Slop−0.9σ
- Scannable−0.3σ
- Answer First+0.0σ
- Truthful+1.4σ
- Literate+1.3σ
- Disclaims Assumptions+1.3σ
- Direct+1.1σ
- Admits Uncertainty+1.1σ
- Resource-Specific−1.3σ
- Slop−1.0σ
- Metaphorical−0.9σ
- Friendly−0.5σ
- Scannable−0.5σ
- Friendly+1.8σ
- Emotionally Intelligent+1.7σ
- Actionable Guidance+1.5σ
- Pragmatic+1.4σ
- Evidence-Based+1.4σ
- Direct−0.8σ
- Literate−0.1σ
- Direct+1.7σ
- Sincere+1.2σ
- Answer First+1.1σ
- Literate+1.1σ
- Natural Sounding+0.8σ
- Sycophantic−1.8σ
- Slop−1.5σ
- Friendly−1.3σ
- Scannable−1.1σ
- Resource-Specific−1.0σ
- Bottom-Line Summary+1.4σ
- Admits Uncertainty+1.2σ
- Confident+1.1σ
- Flags Nuances+1.1σ
- Actionable Guidance+0.9σ
- Friendly−1.4σ
- Sycophantic−1.0σ
- Emotionally Intelligent−0.8σ
- Open-Ended−0.7σ
- Metaphorical−0.5σ
- Natural Sounding+1.9σ
- Lexically Varied+1.6σ
- Eloquent+1.6σ
- Sincere+1.5σ
- Constructive Pushback+1.4σ
- Actionable Guidance−1.0σ
- Sycophantic−0.7σ
- Slop−0.7σ
- Bottom-Line Summary−0.5σ
- Resource-Specific−0.5σ
- Scannable+1.6σ
- Open-Ended+1.1σ
- Admits Uncertainty+0.5σ
- Constructive Pushback+0.5σ
- Slop+0.4σ
- Answer First−1.5σ
- Opinionated−1.3σ
- Actionable Guidance−1.1σ
- Confident−1.0σ
- Sycophantic−1.0σ
평가 살펴보기
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