The BiGGen Bench: A Principled Benchmark for Fine-grained Evaluation of Language Models with Language Models
This addresses the problem of coarse and biased evaluation in language models for researchers and developers, though it is incremental as it builds on existing benchmarking approaches.
The paper tackles the challenge of evaluating language models by introducing the BiGGen Bench, a benchmark that assesses nine distinct capabilities across 77 tasks using instance-specific criteria, and applies it to evaluate 103 frontier LMs with five evaluator LMs.
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.