The BiGGen Bench: A Principled Benchmark for Fine-grained Evaluation of Language Models with Language Models
<|reference_start|>The BiGGen Bench: A Principled Benchmark for Fine-grained Evaluation of Language Models with Language Models: As language models (LMs) become capable of handling a wide range of tasks, their evaluation is becoming as challenging as their development. Most generation benchmarks currently assess LMs using abstract evaluation criteria like helpfulness and harmlessness, which often lack the flexibility and granularity of human assessment. Additionally, these benchmarks tend to focus disproportionately on specific capabilities such as instruction following, leading to coverage bias. To overcome these limitations, we introduce the BiGGen Bench, a principled generation benchmark designed to thoroughly evaluate nine distinct capabilities of LMs across 77 diverse tasks. A key feature of the BiGGen Bench is its use of instance-specific evaluation criteria, closely mirroring the nuanced discernment of human evaluation. We apply this benchmark to assess 103 frontier LMs using five evaluator LMs. Our code, data, and evaluation results are all publicly available at https://github.com/prometheus-eval/prometheus-eval/tree/main/BiGGen-Bench.<|reference_end|>
@article{kim2024the,
    title={The BiGGen Bench: A Principled Benchmark for Fine-grained Evaluation of
  Language Models with Language Models},
    author={Seungone Kim, Juyoung Suk, Ji Yong Cho, Shayne Longpre, Chaeeun Kim,
  Dongkeun Yoon, Guijin Son, Yejin Cho, Sheikh Shafayat, Jinheon Baek, Sue Hyun
  Park, Hyeonbin Hwang, Jinkyung Jo, Hyowon Cho, Haebin Shin, Seongyun Lee,
  Hanseok Oh, Noah Lee, Namgyu Ho, Se June Joo, Miyoung Ko, Yoonjoo Lee,
  Hyungjoo Chae, Jamin Shin, Joel Jang, Seonghyeon Ye, Bill Yuchen Lin, Sean
  Welleck, Graham Neubig, Moontae Lee, Kyungjae Lee, Minjoon Seo},
    journal={arXiv preprint arXiv:2406.05761},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.05761},
    primaryClass={cs.CL}
}