Gemma-7b-it · SORRY-Bench · Base harmful compliance · original prompts ↗
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Gemma-7b-it
6 benchmarks · 10 observations · 4 sources
Scores at a glance
6 benchmarks · large dot: Gemma-7b-it; small dots: closest models where reported
HumanEval+ ↗25%
gemma-7b-it · HumanEval+ · Pass@1 · EvalPlus · official result data ↗
MBPP+ ↗36.8%
gemma-7b-it · MBPP+ · Pass@1 · EvalPlus · official result data ↗
HumanEval ↗28.7%
gemma-7b-it · HumanEval · Pass@1 · EvalPlus · official result data ↗
WildBench ↗23.9 points
Gemma-7B-it · WildBench · WB-Score · reported comparison ↗
BhasaBench / IndicGenBench ↗11.6 points
Gemma-7B-IT · BhasaBench / IndicGenBench · CrossSum-In · one-shot evaluation ↗
4 more reported results
18.6 points · Gemma-7B-IT · BhasaBench / IndicGenBench · Flores-In · one-shot evaluation · IndicGenBench: A Multilingual Benchmark to Evaluate Generation Capabilities of LLMs on Indic Languages ↗
35.3 points · Gemma-7B-IT · BhasaBench / IndicGenBench · XQuAD-In · one-shot evaluation · IndicGenBench: A Multilingual Benchmark to Evaluate Generation Capabilities of LLMs on Indic Languages ↗
13.5 points · Gemma-7B-IT · BhasaBench / IndicGenBench · XorQA-In-Xx · one-shot evaluation · IndicGenBench: A Multilingual Benchmark to Evaluate Generation Capabilities of LLMs on Indic Languages ↗
24.8 points · Gemma-7B-IT · BhasaBench / IndicGenBench · XorQA-In-En · one-shot evaluation · IndicGenBench: A Multilingual Benchmark to Evaluate Generation Capabilities of LLMs on Indic Languages ↗
Closest models by score
Similar positions on shared benchmark leaderboards. Smaller gaps are closer.
4 of 6 shared benchmark comparisons
How similarity is measured
Mean absolute gap in percentile positions on shared benchmark settings; lower is closer. At least 60% overlap is required, with a minimum of three when available. Independent evaluations are used when there are at least three; otherwise published report comparisons are used. Repeated model observations within one setting are averaged before ranking. Writing-style traits and composite indices are excluded. This describes similarity, not an overall quality ranking.
Sources
Every plotted observation links to its evaluation. Reported configurations, benchmark versions and task subsets can differ; similarity describes shared scores, not training lift.
- IndicGenBench: A Multilingual Benchmark to Evaluate Generation Capabilities of LLMs on Indic Languages — published result table ↗
- WildBench: Benchmarking LLMs with Challenging Tasks from Real Users in the Wild — published result table ↗
All 9 authors
Lin, Bill Yuchen, Deng, Yuntian, Chandu, Khyathi, Brahman, Faeze, Ravichander, Abhilasha, Pyatkin, Valentina, Dziri, Nouha, Bras, Ronan Le, Choi, Yejin
- SORRY-Bench: Systematically Evaluating Large Language Model Safety Refusal — published result table ↗
All 16 authors
Tinghao Xie, Xiangyu Qi, Yi Zeng, Yangsibo Huang, Udari Madhushani Sehwag, Kaixuan Huang, Luxi He, Boyi Wei, Dacheng Li, Ying Sheng, Ruoxi Jia, Bo Li, Kai Li, Danqi Chen, Peter Henderson, Prateek Mittal
- Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation — published result table ↗
Names used in source tables
Gemma-7B-IT · Gemma-7B-it · Gemma-7b-it · gemma-7b-it