Do Large Language Models understand how to be judges?

Wed, 01 Oct 2025 00:00:00 +0000

Research setting

An LLM judge is a large language model (LLM) that reads a generated text and grades it, instead of a human reviewer. This paper studies LLM judges for news summaries. The judge receives an Italian news article and a summary, and it gives a score from 1 to 5 with a written explanation. The score follows a rubric, a list of quality criteria in which each criterion has a definition, sub-criteria, and a description of every score level. A good judge gives scores close to those of a human expert.

Article and summary An Italian news article and one summary of it.
Rubric prompt One criterion, its sub-criteria, and examples.
LLM judge Reads both texts and applies the criterion.
Score and explanation A score from 1 to 5 and the reason for it.

The judging task studied in the paper. The prompt asks for a JSON answer with a score and an explanation in Italian (Appendix A of the paper).

Terms used on this page:

Few-shot prompting shows the model a few solved examples inside the prompt, without any training.
MAE (mean absolute error) is the average distance between the model score and the expert score. Lower is better.
Spearman’s ρ measures whether the model ranks the summaries in the same order as the expert. It ranges from -1 to 1, and 0 means no relation.
Positive bias means that a judge gives higher scores than the expert.

Motivation

Overlap metrics such as ROUGE and BLEU count shared words, so they miss whether a summary is faithful or useful to its reader. Human evaluation captures these qualities, but it is costly, inconsistent, and hard to scale. LLM judges promise consistent and cheap scores, but only if they understand the language and the structure of the criteria they receive. Public summarization benchmarks are also a weak test, because their reference summaries often miss editorial standards and models may have seen them during training.

Approach	No judge training	One model per judgment	Criteria with explicit sub-criteria	Test texts unseen in training
Prompted judges	yes	yes	not discussed	not discussed
Fine-tuned judges	no	yes	not discussed	not discussed
Multi-agent judges	not discussed	no	not discussed	not discussed
G-Eval criteria	not discussed	not discussed	partially	not discussed
This paper This work	yes	yes	yes	yes

How the paper positions itself against the related work it discusses (Sections 2 and 3 of the paper).

Objective. Test whether LLMs, prompted with a detailed editorial rubric, understand its criteria well enough to score summaries and explain their scores as a human expert would.

Approach

The rubric comes from the practice of professional editors and has five criteria.

Criterion	What the judge checks
Coherence	The summary reads as a logical, connected narrative.
Consistency	Every factual claim is supported by the article.
Fluency	Grammar, spelling, punctuation, and phrasing are correct.
Relevance	The summary keeps the core points and drops filler.
Ordering	Key points follow the order of the article.

The authors pick 10 Italian news articles published after the training cutoff of every tested model. GPT-4o writes 50 summaries, each made to show a set quality level on one criterion, and humans review and correct them. An expert scores every summary on all five criteria, which gives 250 reference scores. The judges are 24 models: open models of five families (DeepSeek, Gemma 3, Llama 3, Phi 4, Qwen 3) from 0.6B to 14B parameters, and seven OpenAI GPT models. No judge is fine-tuned: each one only sees the rubric prompt with few-shot examples.

1. Write the summaries

Article Data
GPT-4o Frozen
Summary Human
Expert
LLM judge

GPT-4o summarizes each article several times, with a target quality level for one criterion in each summary. Humans check and correct the results.

2. Score with the expert

Article Data
GPT-4o
Summary Data
Expert Human
LLM judge

An expert annotator scores each of the 50 summaries from 1 to 5 on all five criteria.

3. Score with the judges

Article Data
GPT-4o
Summary Data
Expert
LLM judge Frozen

Each judge model receives the rubric prompt and few-shot examples, then scores every summary on every criterion.

4. Compare the scores

Article
GPT-4o
Summary Data
Expert Data
LLM judge Data

The judge scores are compared with the expert scores through MAE and Spearman’s ρ.

Human work Frozen Data

How the test is built and run. No model is trained at any step (Sections 3 and 4 of the paper).

Results

The authors compare each judge with the expert over the 250 scores, through MAE and Spearman’s ρ. They also compare the judges with one another, through Spearman’s ρ between their scores.

0.277highest Spearman’s ρ with the expert (GPT o4 mini)
1.04lowest MAE on the 1 to 5 scale (GPT o4 mini)
0.810Spearman’s ρ between two judges, GPT-4.1 and GPT-4o

Family	Model	MAE	Spearman’s ρ	p-value
DeepSeek	1.5B	1.47	0.008	0.894
	7B	1.71	0.031	0.627
	8B	1.21	-0.018	0.773
	14B	1.05	0.031	0.628
Gemma 3	1B	1.84	-0.078	0.222
	4B	1.38	-0.179	0.005
	12B	1.53	0.010	0.878
Llama 3	1B	1.34	-0.005	0.936
	3B	1.51	0.027	0.666
	8B	1.47	-0.039	0.535
Phi 4	3.8B	1.44	0.219	0.000
	14B	1.31	0.010	0.874
Qwen 3	0.6B	1.36	0.137	0.031
	1.7B	1.24	0.040	0.528
	4B	1.54	0.048	0.453
	8B	1.49	0.065	0.303
	14B	1.46	0.114	0.072
GPT	4o	1.41	0.064	0.314
	4o mini	1.15	-0.032	0.611
	4.1	1.25	0.095	0.136
	4.1 mini	1.42	0.173	0.006
	4.1 nano	1.24	0.034	0.594
	o3 mini	1.31	0.238	0.000
	o4 mini	1.04	0.277	0.000

Agreement of each judge with the expert (Table 1 of the paper).

Agreement with the expert is weak. The best Spearman’s ρ is 0.277, and most values are close to 0. A larger model often makes smaller errors, as in DeepSeek, where MAE drops from 1.47 at 1.5B to 1.05 at 14B, but its ranking does not improve. In the Phi 4 family the smaller model ranks better, with ρ = 0.219 at 3.8B against 0.010 at 14B. Large judges agree more with one another than with the expert, up to ρ = 0.810 between GPT-4.1 and GPT-4o.

Score counts of the expert (blue) and of three judges (orange) for fluency and coherence. The judges pile up on scores 4 and 5 (Figure 6 of the paper).

Almost every judge gives higher scores than the expert, most of all for fluency and coherence. The few-shot examples cover the whole 1 to 5 scale in equal amounts, so the authors trace this positive bias to training data and alignment rather than to the prompt.

Takeaways