Datasets:

M2L-Lab
/

Novelty-Reviewer-Dataset

---
license: cc-by-nc-4.0
language:
- en
pretty_name: Novelty Reviewer Dataset
size_categories:
- 10K<n<100K
task_categories:
- text-generation
- text-classification
annotations_creators:
- machine-generated
language_creators:
- expert-generated
multilinguality:
- monolingual
source_datasets:
- original
tags:
- peer-review
- novelty
- originality
- scientific-documents
- scholarly-document-processing
- openreview
- iclr
- neurips
configs:
- config_name: default
  default: true
  data_files:
  - split: train
    path: data/train-*.parquet
  - split: test
    path: data/test-*.parquet
- config_name: baseline_reviews
  data_files:
  - split: test
    path: baseline_reviews/test-*.parquet
---
# Novelty Reviewer Dataset

**A large-scale dataset of expert novelty judgments from the peer reviews of ICLR and NeurIPS submissions.**

The dataset links 37,899 submissions to the novelty assessments in their 102,021 official reviews. For every
submission it provides the reviews with their numerical scores, the passages in which reviewers assess novelty, and
a paper-level novelty label with a written justification. It supports research on automated novelty assessment,
review generation, related-work retrieval, and the analysis of peer review itself.

| | |
|---|---|
| **Paper** | [*Are We Truly Innovating? A Qualitative and Quantitative Study of Originality in AI Research Papers*](https://arxiv.org/abs/2602.06054) |
| **Code** | [github.com/AbeerMostafa/Novelty-Reviewer](https://github.com/AbeerMostafa/Novelty-Reviewer) |
| **Model** | [AbeerMostafa/Novelty_Reviewer](https://huggingface.co/AbeerMostafa/Novelty_Reviewer) |
| **License** | CC BY-NC 4.0 |
| **Language** | English |

## Dataset at a glance

| | |
|---|---|
| **Submissions** | 37,899 |
| **Official reviews** | 102,021 (1–7 per submission, mean 2.7) |
| **Venues** | ICLR 2022–2025, NeurIPS 2022–2025 |
| **Labels** | Expert-Consensus Novelty Alignment (ECNA) score in {−1, 0, 1, 2}, with justification |
| **Splits** | 31,411 train / 6,488 test; 500-paper evaluation subset |
| **Format** | Parquet, one record per submission |

| Venue | 2022 | 2023 | 2024 | 2025 | Total |
|---|---:|---:|---:|---:|---:|
| ICLR | 2,420 | 3,431 | 6,644 | 10,474 | 22,969 |
| NeurIPS | 2,543 | 3,108 | 3,819 | 5,460 | 14,930 |

## Quick start

```python
from datasets import load_dataset
ds = load_dataset("AbeerMostafa/Novelty-Reviewer-Dataset")
paper = ds["test"][0]
print(paper["title"])
print(paper["novelty_score"], paper["novelty_score_justification"])
# The 500 papers evaluated in the paper
benchmark = ds["test"].filter(lambda r: r["in_eval_subset"])
```

The [companion package](https://github.com/AbeerMostafa/Novelty-Reviewer) adds helpers (`load_novelty_dataset`,
`eval_subset`, `reviews_frame`), PDF download from OpenReview, and tutorial notebooks.

## Novelty labels

Each submission carries an **Expert-Consensus Novelty Alignment (ECNA)**, derived from the stance of its reviewers:

| Score | Label | Definition | Submissions |
|:---:|---|---|---:|
| −1 | Not novel | The work is derivative and largely replicates existing approaches. | 2,924 (7.7 %) |
| 0 | Limited novelty | The work makes minor variations of known methods. | 7,606 (20.1 %) |
| 1 | Moderately novel | The work is original but overlaps notably with prior work or mainly extends existing ideas. | 24,177 (63.8 %) |
| 2 | Highly novel | The work introduces fundamentally new ideas, methods, or problem formulations. | 3,183 (8.4 %) |

The labels reflect reviewing practice at selective venues: most submissions are judged moderately novel, and both
extremes are rare. Reviewer agreement varies: all reviewers agree for 18,727 submissions, a majority agrees for
12,477, and opinions are evenly split for 6,695.

## Dataset structure

### `default`: one record per submission

| Field | Type | Description |
|---|---|---|
| `paper_id` | string | OpenReview identifier; the paper is at `https://openreview.net/forum?id=<paper_id>` |
| `title` | string | Title from OpenReview |
| `abstract` | string | Abstract from OpenReview; links are replaced by `[URL]` |
| `venue`, `year` | string, int16 | `ICLR` or `NeurIPS`; conference year |
| `split` | string | `train` or `test` |
| `in_eval_subset` | bool | Marks the 500 test submissions evaluated in the paper |
| `num_reviews` | int8 | Number of official reviews |
| `reviews` | list of struct | Official reviews (see below) |
| `review_template` | string | The venue's review form: section names and score rubrics |
| `novelty_excerpts` | list of string | Verbatim review passages that assess novelty |
| `novelty_extraction_raw` | string | Raw output of the extraction model, from which `novelty_excerpts` is parsed |
| `novelty_score` | int8 | ECNA score in {−1, 0, 1, 2} |
| `novelty_score_justification` | string | Short justification of the score |
| `novelty_detailed_assessment` | string | Detailed paper-level novelty assessment |
| `novelty_summary` | string | Complete generated assessment (score, justification and detailed assessment) |

Each element of `reviews`:

| Field | Type | Description |
|---|---|---|
| `review_text` | string | Full review in markdown, one `##` section per field of the review form |
| `rating`, `confidence` | int8 | Overall rating (`Recommendation` at ICLR 2022–2023) and reviewer confidence |
| `soundness`, `presentation`, `contribution` | int8 | ICLR 2024–2025, NeurIPS 2022–2024 |
| `correctness`, `technical_novelty_and_significance`, `empirical_novelty_and_significance` | int8 | ICLR 2022–2023 |
| `originality`, `significance`, `quality`, `clarity` | int8 | NeurIPS 2025 |

Scores that a venue's review form does not contain are null. NeurIPS 2025 reviews use a different form without
overall rating and confidence, and no review template is provided for them.

### `baseline_reviews`: model-generated reviews
Full reviews generated by five LLMs (`Llama-OpenReviewer-8B`, `Llama-3.1-70B-Instruct`, `Llama-3.1-8B-Instruct`,
`gpt-4o-2024-11-20`, `claude-3.5-sonnet`) for the 400 ICLR 2025 and NeurIPS 2024 submissions of the
OpenReviewer benchmark (Idahl & Ahmadi, NAACL 2025). Fields: `paper_id`, `model`, `generated_review`.
### Splits
| Split | Submissions | Description |
|---|---:|---|
| `train` | 31,411 | 25,951 submissions used to train the Novelty Reviewer model, plus NeurIPS 2025 |
| `test` | 6,488 | Held-out submissions from ICLR 2022–2025 and NeurIPS 2022–2024 |
| `test` with `in_eval_subset` | 500 | Evaluation subset of the paper |
Splits are defined per submission, so all reviews of a paper fall into the same split. NeurIPS 2025 was collected
after the experiments of the paper and is released as additional training data.
## Dataset creation
### Source data
Submissions and official reviews were collected through the OpenReview API. Titles and abstracts are the official
OpenReview metadata. Full paper texts are not redistributed; they remain available on OpenReview, and the
companion code downloads and converts them to the input format of the model.
### Annotation process
Labels were generated in two stages with Llama-3.1-8B-Instruct:
1. **Extraction.** For each review, the model extracts every passage that bears on novelty, both explicit judgments
   and implicit signals such as comparisons with prior work or criticism of incremental contributions. Passages are
   copied verbatim (greedy decoding).
2. **Aggregation.** For each submission, the model reads all reviews together with per-review novelty stances and
   writes a paper-level assessment and an ECNA score (temperature 0.3, top-p 0.9). The score reflects only the
   reviewers' stance, not the model's own judgment.
All prompts are published in the code repository.
### Quality validation
* **Fidelity.** On 500 sampled submissions, each sentence of the generated assessment was matched to its most
  similar review sentence; the mean cosine similarity is 0.78, and an entailment–contradiction NLI score between
  assessments and reviews is 0.79.
* **Consistency with reviewer scores.** Averaged per submission, ECNA labels correlate with the reviewers'
  *technical novelty* score (Spearman ρ = 0.63, ICLR 2022–2023, 5,851 submissions) and *contribution* score
  (ρ = 0.52, ICLR 2024–2025 and NeurIPS 2022–2024, 26,588 submissions). The aggregation model reads the full
  reviews, which contain these scores, so this is a consistency check rather than independent validation.
## Personal and sensitive information
The dataset is built from public OpenReview data, and identifying information was removed from all text fields:
* Full paper texts are excluded, since camera-ready versions list authors, affiliations and e-mail addresses.
* Each text field (titles, abstracts, reviews, extractions, assessments, baseline reviews) was matched against the
  author list of its submission. Author names, including reversed and initialized forms, are replaced by
  `[REDACTED]`; e-mail addresses are removed.
* All links in abstracts, and links in other fields that contain an author's name, are replaced by `[URL]`.
* 7,297 redactions were made in 6,132 submissions. Reviewer identities are not part of the data.
Citations by surname (e.g. "Chen et al.") are kept. Two submissions are no longer visible on OpenReview and could
not be checked against their author lists.
## Data quality
The extraction model returns JSON, from which `novelty_excerpts` is parsed. Outcomes per submission:

| Outcome | Submissions | Meaning |
|---|---:|---|
| Valid | 30,655 | Parsed directly |
| Repaired | 1,345 | Parsed after escaping LaTeX backslashes or closing brackets |
| Repaired, truncated | 200 | Output was cut off; the last excerpt may be incomplete |
| Recovered | 118 | Excerpts recovered line by line |
| Failed | 121 | Not parsable; `novelty_excerpts` is empty and the output is in `novelty_extraction_raw` |
| Not extracted | 5,460 | NeurIPS 2025; extraction to be added in a later version |

625 assessments do not follow the justification/detailed-assessment format; their full text is in
`novelty_summary`. Nine NeurIPS 2025 assessments contain an out-of-scale score and have `novelty_score` set to null.

## Considerations for using the data

**Intended uses.** Training and evaluating models that assess or explain research novelty; novelty-aware review
generation; related-work retrieval, using reviewer comparisons with prior work as signals; meta-research on peer
review, such as reviewer agreement and the evolution of novelty judgments.

**Out-of-scope uses.** Automated accept/reject decisions; evaluating or ranking individual researchers; attempts to
re-identify anonymous reviewers or redacted authors.

**Limitations and biases.**

* ECNA labels encode reviewer opinion, not ground-truth novelty, and inherit the subjectivity of peer review.
* Labels are generated by an LLM. They are validated on a sample, not verified for every submission.
* The class distribution is skewed toward moderate novelty.
* The data covers two English-language machine learning venues and may not transfer to other fields.
* Submissions include accepted and rejected papers as available on OpenReview; withdrawn or non-public submissions
  are missing, so the data is not a complete record of each venue.

## License

The dataset is released under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/). Reviews and
abstracts come from [OpenReview](https://openreview.net), under licenses that vary by venue and paper (mostly
CC BY 4.0, some CC BY-NC 4.0); CC BY-NC 4.0 covers all of them. Please credit OpenReview and the original reviewers
and authors. The extractions and assessments were generated with Llama 3.1 (Llama 3.1 Community License).

## Citation

```bibtex
@misc{mostafa2026novelty,
  title         = {Are We Truly Innovating? A Qualitative and Quantitative Study of Originality in AI Research Papers},
  author        = {Mostafa, Abeer and Nguyen, Thi Huyen and Ahmadi, Zahra},
  year          = {2026},
  eprint        = {2602.06054},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CL},
  url           = {https://arxiv.org/abs/2602.06054}
}
```

	---
	license: cc-by-nc-4.0
	language:
	- en
	pretty_name: Novelty Reviewer Dataset
	size_categories:
	- 10K<n<100K
	task_categories:
	- text-generation
	- text-classification
	annotations_creators:
	- machine-generated
	language_creators:
	- expert-generated
	multilinguality:
	- monolingual
	source_datasets:
	- original
	tags:
	- peer-review
	- novelty
	- originality
	- scientific-documents
	- scholarly-document-processing
	- openreview
	- iclr
	- neurips
	configs:
	- config_name: default
	default: true
	data_files:
	- split: train
	path: data/train-*.parquet
	- split: test
	path: data/test-*.parquet
	- config_name: baseline_reviews
	data_files:
	- split: test
	path: baseline_reviews/test-*.parquet
	---

	# Novelty Reviewer Dataset

	A large-scale dataset of expert novelty judgments from the peer reviews of ICLR and NeurIPS submissions.

	The dataset links 37,899 submissions to the novelty assessments in their 102,021 official reviews. For every
	submission it provides the reviews with their numerical scores, the passages in which reviewers assess novelty, and
	a paper-level novelty label with a written justification. It supports research on automated novelty assessment,
	review generation, related-work retrieval, and the analysis of peer review itself.

	\| \| \|
	\|---\|---\|
	\| Paper \| [Are We Truly Innovating? A Qualitative and Quantitative Study of Originality in AI Research Papers](https://arxiv.org/abs/2602.06054) \|
	\| Code \| [github.com/AbeerMostafa/Novelty-Reviewer](https://github.com/AbeerMostafa/Novelty-Reviewer) \|
	\| Model \| [AbeerMostafa/Novelty_Reviewer](https://huggingface.co/AbeerMostafa/Novelty_Reviewer) \|
	\| License \| CC BY-NC 4.0 \|
	\| Language \| English \|

	## Dataset at a glance

	\| \| \|
	\|---\|---\|
	\| Submissions \| 37,899 \|
	\| Official reviews \| 102,021 (1–7 per submission, mean 2.7) \|
	\| Venues \| ICLR 2022–2025, NeurIPS 2022–2025 \|
	\| Labels \| Expert-Consensus Novelty Alignment (ECNA) score in {−1, 0, 1, 2}, with justification \|
	\| Splits \| 31,411 train / 6,488 test; 500-paper evaluation subset \|
	\| Format \| Parquet, one record per submission \|

	\| Venue \| 2022 \| 2023 \| 2024 \| 2025 \| Total \|
	\|---\|---:\|---:\|---:\|---:\|---:\|
	\| ICLR \| 2,420 \| 3,431 \| 6,644 \| 10,474 \| 22,969 \|
	\| NeurIPS \| 2,543 \| 3,108 \| 3,819 \| 5,460 \| 14,930 \|

	## Quick start

	```python
	from datasets import load_dataset

	ds = load_dataset("AbeerMostafa/Novelty-Reviewer-Dataset")
	paper = ds["test"][0]
	print(paper["title"])
	print(paper["novelty_score"], paper["novelty_score_justification"])

	# The 500 papers evaluated in the paper
	benchmark = ds["test"].filter(lambda r: r["in_eval_subset"])
	```

	The [companion package](https://github.com/AbeerMostafa/Novelty-Reviewer) adds helpers (`load_novelty_dataset`,
	`eval_subset`, `reviews_frame`), PDF download from OpenReview, and tutorial notebooks.

	## Novelty labels

	Each submission carries an Expert-Consensus Novelty Alignment (ECNA), derived from the stance of its reviewers:

	\| Score \| Label \| Definition \| Submissions \|
	\|:---:\|---\|---\|---:\|
	\| −1 \| Not novel \| The work is derivative and largely replicates existing approaches. \| 2,924 (7.7 %) \|
	\| 0 \| Limited novelty \| The work makes minor variations of known methods. \| 7,606 (20.1 %) \|
	\| 1 \| Moderately novel \| The work is original but overlaps notably with prior work or mainly extends existing ideas. \| 24,177 (63.8 %) \|
	\| 2 \| Highly novel \| The work introduces fundamentally new ideas, methods, or problem formulations. \| 3,183 (8.4 %) \|

	The labels reflect reviewing practice at selective venues: most submissions are judged moderately novel, and both
	extremes are rare. Reviewer agreement varies: all reviewers agree for 18,727 submissions, a majority agrees for
	12,477, and opinions are evenly split for 6,695.

	## Dataset structure

	### `default`: one record per submission

	\| Field \| Type \| Description \|
	\|---\|---\|---\|
	\| `paper_id` \| string \| OpenReview identifier; the paper is at `https://openreview.net/forum?id=<paper_id>` \|
	\| `title` \| string \| Title from OpenReview \|
	\| `abstract` \| string \| Abstract from OpenReview; links are replaced by `[URL]` \|
	\| `venue`, `year` \| string, int16 \| `ICLR` or `NeurIPS`; conference year \|
	\| `split` \| string \| `train` or `test` \|
	\| `in_eval_subset` \| bool \| Marks the 500 test submissions evaluated in the paper \|
	\| `num_reviews` \| int8 \| Number of official reviews \|
	\| `reviews` \| list of struct \| Official reviews (see below) \|
	\| `review_template` \| string \| The venue's review form: section names and score rubrics \|
	\| `novelty_excerpts` \| list of string \| Verbatim review passages that assess novelty \|
	\| `novelty_extraction_raw` \| string \| Raw output of the extraction model, from which `novelty_excerpts` is parsed \|
	\| `novelty_score` \| int8 \| ECNA score in {−1, 0, 1, 2} \|
	\| `novelty_score_justification` \| string \| Short justification of the score \|
	\| `novelty_detailed_assessment` \| string \| Detailed paper-level novelty assessment \|
	\| `novelty_summary` \| string \| Complete generated assessment (score, justification and detailed assessment) \|

	Each element of `reviews`:

	\| Field \| Type \| Description \|
	\|---\|---\|---\|
	\| `review_text` \| string \| Full review in markdown, one `##` section per field of the review form \|
	\| `rating`, `confidence` \| int8 \| Overall rating (`Recommendation` at ICLR 2022–2023) and reviewer confidence \|
	\| `soundness`, `presentation`, `contribution` \| int8 \| ICLR 2024–2025, NeurIPS 2022–2024 \|
	\| `correctness`, `technical_novelty_and_significance`, `empirical_novelty_and_significance` \| int8 \| ICLR 2022–2023 \|
	\| `originality`, `significance`, `quality`, `clarity` \| int8 \| NeurIPS 2025 \|

	Scores that a venue's review form does not contain are null. NeurIPS 2025 reviews use a different form without
	overall rating and confidence, and no review template is provided for them.

	### `baseline_reviews`: model-generated reviews

	Full reviews generated by five LLMs (`Llama-OpenReviewer-8B`, `Llama-3.1-70B-Instruct`, `Llama-3.1-8B-Instruct`,
	`gpt-4o-2024-11-20`, `claude-3.5-sonnet`) for the 400 ICLR 2025 and NeurIPS 2024 submissions of the
	OpenReviewer benchmark (Idahl & Ahmadi, NAACL 2025). Fields: `paper_id`, `model`, `generated_review`.

	### Splits

	\| Split \| Submissions \| Description \|
	\|---\|---:\|---\|
	\| `train` \| 31,411 \| 25,951 submissions used to train the Novelty Reviewer model, plus NeurIPS 2025 \|
	\| `test` \| 6,488 \| Held-out submissions from ICLR 2022–2025 and NeurIPS 2022–2024 \|
	\| `test` with `in_eval_subset` \| 500 \| Evaluation subset of the paper \|

	Splits are defined per submission, so all reviews of a paper fall into the same split. NeurIPS 2025 was collected
	after the experiments of the paper and is released as additional training data.

	## Dataset creation

	### Source data

	Submissions and official reviews were collected through the OpenReview API. Titles and abstracts are the official
	OpenReview metadata. Full paper texts are not redistributed; they remain available on OpenReview, and the
	companion code downloads and converts them to the input format of the model.

	### Annotation process

	Labels were generated in two stages with Llama-3.1-8B-Instruct:

	1. Extraction. For each review, the model extracts every passage that bears on novelty, both explicit judgments
	and implicit signals such as comparisons with prior work or criticism of incremental contributions. Passages are
	copied verbatim (greedy decoding).
	2. Aggregation. For each submission, the model reads all reviews together with per-review novelty stances and
	writes a paper-level assessment and an ECNA score (temperature 0.3, top-p 0.9). The score reflects only the
	reviewers' stance, not the model's own judgment.

	All prompts are published in the code repository.

	### Quality validation

	* Fidelity. On 500 sampled submissions, each sentence of the generated assessment was matched to its most
	similar review sentence; the mean cosine similarity is 0.78, and an entailment–contradiction NLI score between
	assessments and reviews is 0.79.
	* Consistency with reviewer scores. Averaged per submission, ECNA labels correlate with the reviewers'
	technical novelty score (Spearman ρ = 0.63, ICLR 2022–2023, 5,851 submissions) and contribution score
	(ρ = 0.52, ICLR 2024–2025 and NeurIPS 2022–2024, 26,588 submissions). The aggregation model reads the full
	reviews, which contain these scores, so this is a consistency check rather than independent validation.

	## Personal and sensitive information

	The dataset is built from public OpenReview data, and identifying information was removed from all text fields:

	* Full paper texts are excluded, since camera-ready versions list authors, affiliations and e-mail addresses.
	* Each text field (titles, abstracts, reviews, extractions, assessments, baseline reviews) was matched against the
	author list of its submission. Author names, including reversed and initialized forms, are replaced by
	`[REDACTED]`; e-mail addresses are removed.
	* All links in abstracts, and links in other fields that contain an author's name, are replaced by `[URL]`.
	* 7,297 redactions were made in 6,132 submissions. Reviewer identities are not part of the data.

	Citations by surname (e.g. "Chen et al.") are kept. Two submissions are no longer visible on OpenReview and could
	not be checked against their author lists.

	## Data quality

	The extraction model returns JSON, from which `novelty_excerpts` is parsed. Outcomes per submission:

	\| Outcome \| Submissions \| Meaning \|
	\|---\|---:\|---\|
	\| Valid \| 30,655 \| Parsed directly \|
	\| Repaired \| 1,345 \| Parsed after escaping LaTeX backslashes or closing brackets \|
	\| Repaired, truncated \| 200 \| Output was cut off; the last excerpt may be incomplete \|
	\| Recovered \| 118 \| Excerpts recovered line by line \|
	\| Failed \| 121 \| Not parsable; `novelty_excerpts` is empty and the output is in `novelty_extraction_raw` \|
	\| Not extracted \| 5,460 \| NeurIPS 2025; extraction to be added in a later version \|

	625 assessments do not follow the justification/detailed-assessment format; their full text is in
	`novelty_summary`. Nine NeurIPS 2025 assessments contain an out-of-scale score and have `novelty_score` set to null.

	## Considerations for using the data

	Intended uses. Training and evaluating models that assess or explain research novelty; novelty-aware review
	generation; related-work retrieval, using reviewer comparisons with prior work as signals; meta-research on peer
	review, such as reviewer agreement and the evolution of novelty judgments.

	Out-of-scope uses. Automated accept/reject decisions; evaluating or ranking individual researchers; attempts to
	re-identify anonymous reviewers or redacted authors.

	Limitations and biases.

	* ECNA labels encode reviewer opinion, not ground-truth novelty, and inherit the subjectivity of peer review.
	* Labels are generated by an LLM. They are validated on a sample, not verified for every submission.
	* The class distribution is skewed toward moderate novelty.
	* The data covers two English-language machine learning venues and may not transfer to other fields.
	* Submissions include accepted and rejected papers as available on OpenReview; withdrawn or non-public submissions
	are missing, so the data is not a complete record of each venue.

	## License

	The dataset is released under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/). Reviews and
	abstracts come from [OpenReview](https://openreview.net), under licenses that vary by venue and paper (mostly
	CC BY 4.0, some CC BY-NC 4.0); CC BY-NC 4.0 covers all of them. Please credit OpenReview and the original reviewers
	and authors. The extractions and assessments were generated with Llama 3.1 (Llama 3.1 Community License).

	## Citation

	```bibtex
	@misc{mostafa2026novelty,
	title = {Are We Truly Innovating? A Qualitative and Quantitative Study of Originality in AI Research Papers},
	author = {Mostafa, Abeer and Nguyen, Thi Huyen and Ahmadi, Zahra},
	year = {2026},
	eprint = {2602.06054},
	archivePrefix = {arXiv},
	primaryClass = {cs.CL},
	url = {https://arxiv.org/abs/2602.06054}
	}
	```