Prometheus 2: An Open Source Language Model Specialized in Evaluating Other Language Models
About
Proprietary LMs such as GPT-4 are often employed to assess the quality of responses from various LMs. However, concerns including transparency, controllability, and affordability strongly motivate the development of open-source LMs specialized in evaluations. On the other hand, existing open evaluator LMs exhibit critical shortcomings: 1) they issue scores that significantly diverge from those assigned by humans, and 2) they lack the flexibility to perform both direct assessment and pairwise ranking, the two most prevalent forms of assessment. Additionally, they do not possess the ability to evaluate based on custom evaluation criteria, focusing instead on general attributes like helpfulness and harmlessness. To address these issues, we introduce Prometheus 2, a more powerful evaluator LM than its predecessor that closely mirrors human and GPT-4 judgements. Moreover, it is capable of processing both direct assessment and pair-wise ranking formats grouped with a user-defined evaluation criteria. On four direct assessment benchmarks and four pairwise ranking benchmarks, Prometheus 2 scores the highest correlation and agreement with humans and proprietary LM judges among all tested open evaluator LMs. Our models, code, and data are all publicly available at https://github.com/prometheus-eval/prometheus-eval.
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Reward Modeling | RewardBench | Avg Score75.3 | 118 | |
| Reward Modeling | RewardBench | Accuracy83.7 | 70 | |
| Reward Modeling | RewardBench v1.0 (test) | Chat Score0.855 | 27 | |
| Audio QA Correctness Assessment | MMAU and MMAR unseen question-based (test) | Spearman ρ0.7439 | 18 | |
| LLM-as-a-judge evaluation | FLASK | Pearson's r0.512 | 16 | |
| Text Summarization | SummEval Global | Coherence78.4 | 16 | |
| LLM-as-a-judge evaluation | MT-Bench | Pearson's r0.519 | 16 | |
| LLM-as-a-judge evaluation | Vicuna-bench | Pearson Correlation (r)0.488 | 16 | |
| LLM-as-a-judge evaluation | FB Bench (Feedback Bench) | Pearson's r0.845 | 16 | |
| Text Quality Meta-evaluation | SummEval (Local) | Coherence0.623 | 16 |