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Models / Anthropic

Anthropic / MODEL EVIDENCE

Claude

3 benchmarks · 12 observations · 3 sources

Scores at a glance

3 benchmarks · large dot: Claude; small dots: closest models where reported

AlpacaEval ↗88.4%
15.295.3

claude · AlpacaEval · AlpacaEval 1 · GPT-4 judged win rate · original leaderboard example ↗

HaluEval 2.0 ↗31.44%
14.6660.91

Claude · HaluEval 2.0 · Biomedicine · MaHR · published main comparison ↗

9 more reported results

8.25% · Claude · HaluEval 2.0 · Biomedicine · MiHR · published main comparison · The Dawn After the Dark: An Empirical Study on Factuality Hallucination in Large Language Models ↗

39.11% · Claude · HaluEval 2.0 · Finance · MaHR · published main comparison · The Dawn After the Dark: An Empirical Study on Factuality Hallucination in Large Language Models ↗

10.56% · Claude · HaluEval 2.0 · Finance · MiHR · published main comparison · The Dawn After the Dark: An Empirical Study on Factuality Hallucination in Large Language Models ↗

21.31% · Claude · HaluEval 2.0 · Science · MaHR · published main comparison · The Dawn After the Dark: An Empirical Study on Factuality Hallucination in Large Language Models ↗

4.78% · Claude · HaluEval 2.0 · Science · MiHR · published main comparison · The Dawn After the Dark: An Empirical Study on Factuality Hallucination in Large Language Models ↗

41.26% · Claude · HaluEval 2.0 · Education · MaHR · published main comparison · The Dawn After the Dark: An Empirical Study on Factuality Hallucination in Large Language Models ↗

11.53% · Claude · HaluEval 2.0 · Education · MiHR · published main comparison · The Dawn After the Dark: An Empirical Study on Factuality Hallucination in Large Language Models ↗

55.39% · Claude · HaluEval 2.0 · Open Domain · MaHR · published main comparison · The Dawn After the Dark: An Empirical Study on Factuality Hallucination in Large Language Models ↗

19.5% · Claude · HaluEval 2.0 · Open Domain · MiHR · published main comparison · The Dawn After the Dark: An Empirical Study on Factuality Hallucination in Large Language Models ↗

Closest models by score

Similar positions on shared benchmark leaderboards. Smaller gaps are closer.

There are not enough shared scored settings to identify close comparisons reliably.

Filter comparisons to this model →

Sources

Every plotted observation links to its evaluation. Reported configurations, benchmark versions and task subsets can differ; similarity describes shared scores, not training lift.

  1. The Dawn After the Dark: An Empirical Study on Factuality Hallucination in Large Language Models — published result table ↗

    Li, Junyi, Chen, Jie, Ren, Ruiyang, Cheng, Xiaoxue, Zhao, Wayne Xin, Nie, Jian-Yun et al. · 2024

    All 7 authors

    Li, Junyi, Chen, Jie, Ren, Ruiyang, Cheng, Xiaoxue, Zhao, Wayne Xin, Nie, Jian-Yun, Wen, Ji-Rong

  2. Do-Not-Answer: Evaluating Safeguards in LLMs ↗

    Yuxia Wang, Haonan Li, Xudong Han, Preslav Nakov, Timothy Baldwin · 2024

  3. AlpacaEval: An Automatic Evaluator of Instruction-following Models ↗

    Xuechen Li, Tianyi Zhang, Yann Dubois, Rohan Taori, Ishaan Gulrajani, Carlos Guestrin et al. · 2023

    All 8 authors

    Xuechen Li, Tianyi Zhang, Yann Dubois, Rohan Taori, Ishaan Gulrajani, Carlos Guestrin, Percy Liang, Tatsunori B. Hashimoto

Names used in source tables

Claude · claude