arxiv: 2302.04166 · v2 · pith:6UG7QNNZnew · submitted 2023-02-08 · 💻 cs.CL

GPTScore: Evaluate as You Desire

Jinlan Fu , See-Kiong Ng , Zhengbao Jiang , Pengfei Liu This is my paper

Pith reviewed 2026-05-17 17:06 UTC · model grok-4.3

classification 💻 cs.CL

keywords modelsevaluationgenerationgptscorepre-trainedtextachieveevaluate

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The pith

GPTScore uses zero-shot prompting of generative models ranging from 80M to 175B parameters to evaluate text according to arbitrary natural language criteria, tested on 4 tasks, 22 aspects, and 37 datasets.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Evaluating AI-generated text is difficult because quality depends on many subjective aspects such as coherence, relevance, or creativity, and traditional automatic metrics often fail to capture what humans care about. Collecting human ratings to train a new evaluator for each aspect is expensive and slow. GPTScore instead relies on the instruction-following ability of already-trained large models. A user writes a short description of the desired evaluation, for example 'rate how well this summary captures the main points,' and the model returns a numeric score. The authors ran experiments with 19 different models on text generation tasks including summarization and dialogue response. They covered 22 different evaluation aspects across 37 datasets. The main practical benefit is that new evaluation criteria can be defined and used immediately without any additional labeled training data.

Core claim

Experimental results on four text generation tasks, 22 evaluation aspects, and corresponding 37 datasets demonstrate that this approach can effectively allow us to achieve what one desires to evaluate for texts simply by natural language instructions.

Load-bearing premise

That the emergent zero-shot instruction-following abilities of the tested pre-trained models can produce scores that meaningfully reflect the desired evaluation criteria without task-specific fine-tuning or annotated samples.

read the original abstract

Generative Artificial Intelligence (AI) has enabled the development of sophisticated models that are capable of producing high-caliber text, images, and other outputs through the utilization of large pre-trained models. Nevertheless, assessing the quality of the generation is an even more arduous task than the generation itself, and this issue has not been given adequate consideration recently. This paper proposes a novel evaluation framework, GPTScore, which utilizes the emergent abilities (e.g., zero-shot instruction) of generative pre-trained models to score generated texts. There are 19 pre-trained models explored in this paper, ranging in size from 80M (e.g., FLAN-T5-small) to 175B (e.g., GPT3). Experimental results on four text generation tasks, 22 evaluation aspects, and corresponding 37 datasets demonstrate that this approach can effectively allow us to achieve what one desires to evaluate for texts simply by natural language instructions. This nature helps us overcome several long-standing challenges in text evaluation--how to achieve customized, multi-faceted evaluation without the need for annotated samples. We make our code publicly available at https://github.com/jinlanfu/GPTScore.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

GPTScore shows you can prompt LLMs to score text on custom criteria defined in natural language, with broad tests across models and datasets but thin details on the actual score quality.

read the letter

Hey, the main thing with this paper is that it demonstrates using off-the-shelf pre-trained models to score generated text based on whatever evaluation criteria you describe in plain instructions. They test this across 19 models from 80M parameters up to 175B, four generation tasks, 22 aspects, and 37 datasets, and release the code publicly. That setup lets you do customized, multi-aspect evaluation without collecting annotated samples for each new need. The experiments give evidence that zero-shot instruction following can produce usable scores in these cases, which lines up with the claim that emergent abilities handle this without task-specific tuning. The scale of the testing is the strongest part here, covering both smaller models like FLAN-T5 variants and larger ones like GPT-3, and showing the approach generalizes across the reported setups. Releasing code helps with checking the implementation directly. On the softer side, the summary leaves out specifics like exact correlation values with human judgments, the baselines compared against, or any statistical tests, so it's difficult to judge how reliable or superior the scores actually are in practice. The core assumption that the model outputs track the intended criteria holds in the described results without obvious internal contradictions or circular fitting, but independent checks on edge cases or bias in the scoring would clarify the limits. This paper is for NLP folks working on generative models who need more flexible evaluation options than fixed metrics provide. Readers focused on reducing annotation costs or exploring LLM uses for assessment tasks would get the most out of the empirical breadth. It deserves peer review because the practical framing and experiment coverage are substantial enough to benefit from referee input on the validation details.

Referee Report

2 major / 2 minor

Summary. The paper proposes GPTScore, a framework that leverages the zero-shot instruction-following abilities of 19 generative pre-trained models (sizes 80M to 175B) to assign scores to generated texts according to arbitrary natural-language criteria. Experiments cover four text-generation tasks, 22 evaluation aspects, and 37 datasets; the central claim is that this yields effective, customized, multi-faceted evaluation without task-specific fine-tuning or annotated samples.

Significance. If the empirical results hold, the work offers a practical route to annotation-free, instruction-driven evaluation that directly addresses long-standing limitations in NLG assessment. The breadth of models and datasets tested, together with the public code release, supplies concrete empirical support for the utility of emergent abilities in this setting.

major comments (2)

[Experiments] Experiments section: the manuscript asserts that GPTScore tracks desired criteria across 37 datasets, yet the main text and tables do not report the precise correlation coefficients (Pearson, Spearman, or Kendall), the human-judgment collection protocol, or any statistical significance tests; without these quantities the effectiveness claim cannot be quantitatively evaluated.
[§4.2] §4.2 and Table 2: the comparison with baselines is presented only for a subset of aspects; the paper must show that GPTScore remains competitive on the full set of 22 aspects or explicitly state which aspects were omitted and why, as selective reporting directly affects the multi-faceted evaluation claim.

minor comments (2)

[Abstract] Abstract: the four tasks are not named; adding their names would immediately clarify the scope of the evaluation.
[Method] Notation: the prompt template is described in prose but never shown as a boxed example; including one concrete prompt per task would remove ambiguity for readers who wish to replicate.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thoughtful and constructive comments on our manuscript. We address each of the major comments below and outline the revisions we plan to make to strengthen the paper.

read point-by-point responses

Referee: [Experiments] Experiments section: the manuscript asserts that GPTScore tracks desired criteria across 37 datasets, yet the main text and tables do not report the precise correlation coefficients (Pearson, Spearman, or Kendall), the human-judgment collection protocol, or any statistical significance tests; without these quantities the effectiveness claim cannot be quantitatively evaluated.

Authors: We agree that providing the precise correlation coefficients, details on the human judgment protocol, and statistical significance tests would enhance the quantitative evaluation of our claims. The human-judgment collection protocol is briefly described in the experimental setup, but we will expand this section with more details, including the number of annotators, inter-annotator agreement, and the exact procedure. We will add the specific Pearson, Spearman, and Kendall correlation values for all datasets and aspects to the main tables or a dedicated appendix. Additionally, we will include statistical significance tests (e.g., p-values) comparing GPTScore to baselines. These changes will be incorporated in the revised manuscript. revision: yes
Referee: [§4.2] §4.2 and Table 2: the comparison with baselines is presented only for a subset of aspects; the paper must show that GPTScore remains competitive on the full set of 22 aspects or explicitly state which aspects were omitted and why, as selective reporting directly affects the multi-faceted evaluation claim.

Authors: We appreciate this point regarding potential selective reporting. In §4.2, we focused on a subset of aspects that are commonly evaluated across tasks to provide a clear comparison within the space constraints of the main paper. To fully address the multi-faceted evaluation claim, we will include results for the complete set of 22 aspects in an extended table or appendix in the revised version. We will also add a statement in the main text explaining the selection of the subset for the primary table and confirming that GPTScore performs competitively across all aspects. revision: yes

Circularity Check

0 steps flagged

No significant circularity: method applies off-the-shelf zero-shot prompting without fitted parameters or self-referential reductions

full rationale

The paper presents GPTScore as a prompting-based evaluation framework that directly invokes the emergent zero-shot instruction-following abilities of existing pre-trained models (19 models from 80M to 175B parameters) to produce scores for generated text according to arbitrary natural-language criteria. No equations, parameter fitting, or derivation steps are described that would reduce the output scores back to the paper's own inputs, training data, or prior results by construction. The central claim is substantiated through direct empirical evaluation on 37 datasets spanning four tasks and 22 aspects, with public code release enabling external verification. No self-citation load-bearing steps, uniqueness theorems, or ansatzes are invoked; the approach treats model capabilities as an external, independently available resource rather than deriving them internally.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Central claim rests on the domain assumption that pre-trained generative models possess reliable zero-shot scoring abilities via instructions; no free parameters or invented entities are introduced in the abstract.

axioms (1)

domain assumption Generative pre-trained models possess emergent zero-shot instruction-following abilities that can be directly used for text scoring.
Invoked as the foundation for the GPTScore framework in the abstract.

pith-pipeline@v0.9.0 · 5502 in / 1222 out tokens · 71655 ms · 2026-05-17T17:06:06.842651+00:00 · methodology

discussion (0)

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This nature helps us overcome several long-standing challenges in text evaluation--how to achieve customized, multi-faceted evaluation without the need for annotated samples.

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