Venue: NeurIPS 2021 Track Datasets_and_Benchmarks Round2

Reviews:

final_score: rating: 7: Good paper, accept
review_content: summary and contributions: The paper proposes a curated dataset for structure-to-text tasks, consisting of infoboxes for fictional people paired with their biographies. The construction of this dataset includes both generative language models as well as human annotators. 
strengths: (1) Overall, I think this is a good contribution with clear motivation, well-designed experiments, and in-depth analysis. The proposed dataset will undoubtedly benefit the research community.

(2) The paper is very well-written and easy to follow. The dataset construction process is described clearly, and I agree that human-AI collaboration is a promising direction for dataset construction.

(3) The newly proposed dataset addresses some of the issues in WikiBio which has been discussed in the paper.

(4)  I like the analysis part that explores the performance differences for the same models on the WikiBio dataset and SynthBio dataset using automatic metrics.

(5) I think the “Discussion and broader impact” part is very well-written and makes some points for the value of curating datasets and synthetic data.
weaknesses: The explorations on three potential reasons causing performance differences in Table 6 should take more pages. I think this is a very interesting part and should not be taken lightly.
correctness: Yes, the paper is technically sound.
clarity: Yes, the paper is very well-written.
relation to prior work: Yes, prior works are discussed properly.
additional feedback: See the weaknesses.
review_title: A good contribution

final_score: rating: 7: Good paper, accept
review_content: summary and contributions: The authors propose a structure-to-text dataset, SynthBio, built using structure and text generated from a large language model followed by edits made by human annotators. SynthBio presents a dataset of Wikipedia-infobox like attributes paired with multiple biographies tied to the set of attributes. The individuals in SynthBio are hypothetical individuals allowing the dataset to control for coverage over attributes (along dimensions such as gender, nationality, notability type etc.) and allowing model evaluation where memorizing training data wouldnt help performance on this dataset. The authors also human evaluate the quality of the SynthBio and show it to be of high quality in the eyes of human evaluators. Further they demonstrate that SynthBio also presents higher quality reference text for structure-to-text models compared to prior work in WikiBio.
strengths: - The dataset presents improved reference texts in comparison to WikiBio which will be of value to language generation evaluation.

- The synthetic nature of the dataset allows for control over distribution of individuals over attributes in the dataset which helps control for the biases present in datasets such as WikiBio. The synthetic nature also allows evaluation under a setup where models havent memorized biographies of individuals.

- The paper also provides some novelty in the method of dataset construction for text generation in being generated by human-edits atop generations of an LLM.
weaknesses: 1. While the paper indicates that SynthBio is of high quality in the eyes of human raters, the paper does not present an analysis of two important aspects of the dataset: the effects of edits made by humans to the dataset and any possible biases in the dataset because of the way it was generated using large language models.

2. While the paper presents an approach to build a dataset for a specific structure-to-text application the claims made in the generality of the approach are somewhat unvalidated. For example, lines 25-28 present the method as applicable to a much broader set of datasets ("efﬁcient labeled text dataset curation") than what the paper validates, claiming generality to other language generation tasks would be the extent of what is reasonable. This is exacerbated by the reliance of the approach on large language models, infrastructure that only select research groups have access to. I would ask that the authors tone down claims of generality or provide arguments of reasonable ways the proposed method may be applied in diverse other contexts.

Elaborations regarding the first weakness: 

The paper does not address an important dichotomy about the value of human edits vs the value of model pre-generating text. If we expect to see large edits to a models output one would need to question how much the model helps (in reducing human effort of authoring texts) and hurts (by introducing a bias where text is drawn from the models distribution alone) and if a cheaper model than an LLM may have been used. On the other hand, if the model text is largely unedited or presents trivial edits one must question this datasets utility (and of human evaluation as others have done given that current text generation models fool them easily: [Clark et al. ACL 2021](https://aclanthology.org/2021.acl-long.565/)) for measuring meaningful progress. A sweet spot between these two extremes is likely to exist which the paper does not address. The statistics of edit distances (Table 5) seem to be the one presentation of the effect of edits but these are somewhat hard to place in context for their meaning.

Q1: The supplementary materials detail three kinds of edits that annotators made to the attribute lists and generated bios. Were any statistics on the type of edits collected? Sharing a range of examples of edits made by humans would also be helpful. 

Q2: While the annotator instructions asked annotators to not spend beyond 2 minutes on an edit the average time spent for the biography is about twice that. Does this imply that the text from the LLM required heavier edits? How would the number of edits required compare to text generated from a more broadly accessible model (eg. GPT-2)?

Some experiments which might shed more light on the model biases and effect of edits:

Q3: Did you attempt to perform evaluations (human and automatic of Section 4) on the unedited bios generated from an LLM? If one were to see similar performance as the human edited dataset the edits might seem somewhat unimportant?

Q4: Given that others ([Chintagunta et al NAACL-ws 2021](https://aclanthology.org/2021.nlpmc-1.9/), cited in the Related works) have shown it possible to use GPT3 text as a way to build an augmented summarization dataset, is it reasonable to say that one way to test if the text generated from the LLM is biased in some way would be to fine-tune a T5 (or other model) on unedited attribute lists and bios from the LLMs as augmented data followed by evaluation on SynthBio? 

Q5: Were any attempts made to train classifiers of the kind presented in [Ippolito et al. ACL 2020](https://aclanthology.org/2020.acl-main.164.pdf), which shares authors with this submission, to check if trained classifiers were able to tag text in the edited vs unedited text of the bios as human vs machine written? If the edits made detection of machine text harder it would indicate the value of the human edits.

Of course, other experiments of the authors design could also answer these concerns.
correctness: The dataset seems to be constructed in a sound manner, aside from the critiques raised above.
clarity: The paper is largely well written.
relation to prior work: Related work is discussed with sufficient detail. But, the authors should make note to cite: [Fanton et al, ACL 2021](https://aclanthology.org/2021.acl-long.250.pdf) who present a dataset generated using GPT-2 in a editing loop with human annotators in the context of hate speech counter narrative generation.

additional feedback: Other questions:
Lines 148-151: Questions about few-shot examples:

Q6: How were the few shot examples authored? Did humans generate attributes and bios from scratch, or was a different procedure followed? Please elaborate.

Q7: Were the same few shot examples used for all the biographies in the dataset for a given notability type? 

Q8: Were any experiments conducted on the number of few shot examples needed for reasonable performance or on the effect of using different sets of few-shot examples to generate the bios?

Q9: Will all of the human authored few shot examples be included in the dataset release?
review_title: Paper presenting desirable traits for NLG evaluation alongside concerns in dataset construction.

final_score: rating: 7: Good paper, accept
review_content: summary and contributions: The paper describes the creation of SynthBio, a synthetic dataset of biographies, serving as a companion to the WikiBio dataset. The authors use an AI-human collaborative approach to the creation where a large-scale LM is asked to generate attributes for fictional humans (similar to Wikipedia infoboxes) and biographies based on those attributes and a human annotator is asked to rate and edit both. The resulting dataset contains a more balanced set of attributes compared to WikiBio and in addition it avoids the problem of using real human biographies which can be memorised by large LMs. Both properties make SynthBio an attractive evaluation dataset for data-to-text generation systems.
strengths: The paper presents an interesting methodology for human-AI collaborative dataset generation which should be appealing to many researchers in the field trying to address the problems of memorisation and bias present in modern LMs. The authors take great care to address the ethical concerns arising both from the data and the dataset generation process.
weaknesses: The SynthBio dataset is too small to be used for training (so that its attractive properties can be used directly by the models). In addition, the process described in this work only applies to synthetic data generation which might be a limitation for some researchers. The biggest limitation however is that the main motivation factors for this work haven't been fully explored or aren't fully supported by the experiments. The details are in the correctness and additional feedback sections.
correctness: The dataset generation process is for the most part sound and well designed. The details presented in the paper and the appendices are very thorough. There are some areas that further clarification is needed though. First, the reported perplexity of the bios in Table 1 seems to indicate that the unedited biographies are by far the most fluent whereas the real Wikipedia (human-authored) ones are the least fluent (and the edits of the synthetic bios make them less fluent). These counterintuitive results seem to point to the fact that PPL as measured by GPT-2 is very biased towards LM-generated text. The other, and more important point concerns the attribute distribution. While the construction method allowed for a balanced distribution of all attributes (types, gender, ethnicity etc. could have been controlled for at the prompt stage) and the authors explicitly call out that they ensured an equal gender representation (line 68), it's not clear why there are still imbalances in the distributions as it is evident from Table 1 and Figure 2. There is also lack of reporting around the quality control during the dataset annotation process. The authors note in Appendix B.3 that quality assurance was "performed periodically on the ratings" and that there was a process of adjudication. However, there is no mention of inter-annotator agreement (or adjudication overturn rate if there a single annotator per example), nor of QA during the editing stage.

There are also a few issues/questions with the examples in Table 7 (Appendix A).
- Are these examples from the edited or unedited version of the dataset?
- In all of the examples, the education is marked as PhD - while I appreciate the idea that people from all genders, countries and times can get a PhD, I am hoping that the full dataset contains a more realistic distribution of educational levels. 
- The gender attribute for the last example is capitalised ("Male"); this might be a typo (by the LM?) but it made me wonder whether spelling differences or typos can affect the quality of the produced bios. 
- Also on the same example, the generated bio first sentence refers to the entity by their last name only. Is this a typical variant in the generated text? I can see how it might lead to reference ambiguities downstream.
- In the first example, the fact that Tacettin Guntekin's notable works are books isn't mentioned in the attribute list. Is this a type of an allowed inference on the part of the model (if this is an edited example)?
- In the third example, there is a missing space after Grecia, but more importantly, the first preposition is "He" rather than "They". Is this an uncaught error by the model (if this is an edited example)?
clarity: The paper is generally well written but there are a number of minor issues/typos:

- The tables and figures should be closer to the text that references them.
- Line 9: "comprised of" -> "composed of" or "comprising" (without of)
- Add a reference to Tables 3 and Figure 2 in lines 34-5 to match the reference to the statistics of Table 1.
- Figure 1 has multiple references to terms and methods that are not defined until later in the paper (few-shot examples, LLM, LLM-Dialog) - I really like the shape/colour coding of the stages though.
- Line 45: "is scarce" -> "are scarce" 
- Table 2: it's not clear what the non-highlighted bits of text are? Are they all inaccurate (hallucinations)? If so, I would rephrase the "somewhat accurate" comment in the caption.
- I don't think there "infinite ways" to express a list of attributes in natural language (line 72).
- Line 122: According Figure 1 and previous description in the text, the model is not queried for an individual's nationality, gender and birthdate; they are sampled programmatically (step 1b)
- Appendices:
 - Circular reference of Appendix A.1 in Appendix A.1 (line 592)
 - Licensing information marked as TODO (line 741)
 - Missing Table reference on line 745
relation to prior work: Yes, there is a discussion on how the proposed approach and dataset differ from similar works in curated data-to-text dataset, synthetic dataset generation and human-AI collaborative dataset generation.
additional feedback: The main stated reasons for creating SynthBio (in comparison to WikiBio) is a) to sidestep the problem of memorisation of large LMs and b) to control for noise and biases of naturalistic data. These should be weighed against the cost of creating the dataset (and tradeoffs between size and quality) as opposed to e.g. editing/augmenting the original WikiBio. This is the first missing comparison: what if instead of LM-generated attributes/bios, the annotators corrected the original Wikipedia bios? This of course would leave the problems of skewed attribute distributions, but that would be possible to account for with different test splits each with different criteria (geography, gender, occupation). Beyond the comparison to editing WikiBio, it is interesting that the results in section 4 (lines 205-210) suggest that these biases of WikiBio don't seem to affect the performance of the trained models w.r.t these attributes (of course it's still a useful diagnostic). Finally, regarding the element of memorisation, there is a missing comparison with a non-fine-tuned version of T5. This would demonstrate how much of the performance on WikiBio is due to pure memorisation (the kind shown in Table 2) compared to memorisation due to overfitting.

Regarding the edits done by the annotators, it would be good to get an idea of the distribution of edit types (deleting hallucinated information, adding missing information, fixing grammar or typos, etc).
review_title: Interesting dataset generation method with a few limitations

final_score: rating: 7: Good paper, accept
review_content: summary and contributions: The paper mainly proposes a workflow in which human raters collaborate with a large language model to efficiently curate a high-quality labeled text dataset. The study applies the workflow to create a case study in curating a new structure-to-text dataset, SynthBio. The main technical point is the use of editing instead of creating new instances, which is faster and more accurate. And the authors argue that this method will reduce bias in training which is the overall goal of the workflow.

While the goal of reducing bias in training is of importance and the study has performed various human annotations, there are serious problems in its foundations, especially regarding bias studies, that make the result and the overall workflow unacceptable. As the main two results of the paper, it is stated in the abstract that "We show that our dataset of fictional biographies is less noisy than WikiBio, and also more balanced with respect to gender and nationality".
strengths: - The goal of reducing bias in training is of importance. 
- The study has performed human annotations. 
- The paper is well-written.
weaknesses: These points are not sorted in any way:

1. Let's say that the point of equalizing the numbers (in this paper) is only to help to create more accurate machine learning models via a better training dataset. In the paper, the numbers are considered equal to help reduce bias in training (such as #spies==#artists). This (and many more apparent equalities) does not reflect the world as is and can be argued that it would create worse models in training. For example, the number of males and females and non-binaries are all considered equal in the unedited version (and close after human editing) for all occupations. In an occupation, such as mining, where almost all of the subjects are male, using equal numbers would only mislead the model by removing an important piece of information.

2. The data should reflect reality and if there is a problem or bias in training, it should be handled through various modeling or pre-processing steps. I believe that the data should remain loyal without removing important information or statistics. For example, WikioBio contains 72,831 subjects in the train part, while the novel dataset only includes 2,793. It would be easy to subsample the former to create an unbiased dataset without the need for human annotations. Maybe another case study would have been a stronger choice and would better reflect the results of the proposed workflow.

3. In Figure 2, the distribution of people from the novel dataset is compared with WikioBio according to the world population. The English Wikipedia does not have to reflect the statistics of all populations. For example, there are fewer cabdrivers compared to politicians as famous people, while they are much more in real life. However, if the goal was only to create equal numbers, then there should be equal numbers from each country, which would raise other problems (e.g. #china==#guam is simply biased in sampling, and more problems as stated before).

4. The work argues that equal numbers would lead to better results, but it clearly will not. For example, the number of people from India has equal numbers in the dataset to a very small country. This if this country is split into two or more countries, the number of samples would be multiplied. This means that the same people are represented with more attention, even though nothing has changed. This is more evident in occupations where there are overlapping notability groups, such as "sportsperson", "swimmer", "rugby biography", ... . Perhaps it would be meaningful to find the right method for subsampling, as it would lead to better evaluation results.

5. One very important problem with the current paper is that it assumes that the Wikipedia infobox is complete, cohesive, and accurate, while the biographies are incorrect and should be based on the structured infobox. However, it can be argued (and easily showed) that many of the biographies are continuously edited from valuable sources, while the structured infoboxes in Wikipedia are usually handled by fewer users based on the updated biographies. Thus the base of the paper assumption for the case study is wrong (or unproven).

6. Continuing with notability groups of Appendix 1, there are several overlapping or duplicate notability groups, such as "sportsperson", "swimmer", "rugby biography", ... . In addition, they use different granularity levels (e.g. "college football player" vs "artist" or "officeholder"). Finally, many occupations are missing from the base Wikipedia pages (such as judges and soldiers). This also clearly shows that the infobox can be incorrect on many levels. Further study is required for other properties.

7. Another important point is the way the work and previous works have been evaluated. All works are evaluated based on the evaluation part of the novel dataset. As stated, the current dataset does not reflect statistics of the real world, famous people, or famous people submitted at Wikipedia, thus it seems incorrect.

8. Since the work uses manual editing by human annotators, the problems regarding bias in submitting could still persist. For example, if most annotators are male or with a computer science background or from the USA.

9. The change in model evaluation results could come from several steps: 1) synthetic generation, 2) equal numbers, 3) changing statistics, 4) human annotation, 5) dataset size. I would suggest changing the case study to a less complicated one where you can accurately analyze the impact of each part.


A few points regarding bias in Wikipedia, WikioBio, ... :
1. Wikipedia reflects famous people of the world, not a fair subsample of all populations. By design, it only includes people based on their popularity, not their impact or goodness. Thus the question is, does your work tries to create a fair method for the whole population or only for famous people? If the former, then approaches such as WikioBio cannot be compared in statistics as the goal is different. If the latter, then works such as WikioBio are much closer to the reality of famous people (as collected by the populations) than a synthetic dataset based on what the world statistics should be based on your view of life. Based on equal numbers taken for occupation (e.g. number of spies are equal to the number of researchers in the new dataset) and gender (male/female/non-binaries) in the new dataset, the novel dataset statistics don't reflect the current world or the world that should be. Thus I would not recommend comparing this work with works based on real data.

2. If the point is to show the existing inequalities or biases in Wikipedia subjects or the real world, there are too many existing open problems or specific questions about the current approach or the reality of the world that would be impractical to answer in such a short paper. The first question is, does inequal numbers mean bias.
3. It is true that Wikipedia may not reflect a 100% accurate view of all famous people. As it is mentioned in the paper, most subjects are from the EU and the USA. However, it is not clear how much of this apparent inequality comes from the real world view and how much is from the WIKI contributors. For example, there are many more male researchers and politicians in the world compared to women; thus reflecting this in Wikipedia or related datasets is not a weakness on their parts. As another example, even though there are fewer female subjects in soccer than males, while they are probably close in number in the real world, female sports make much less money than males in reality (through ads, tickets, ...), indicating less popularity of the subjects.
4. It can be agreed or argued that an actress or player from the USA or EU has more chance to be known in other countries (thus passes the notability test) than the opposite. The impact of this point (similar to others) requires separate studies and cannot be identified or resolved in a short unrelated paper.

5. It should be noted that the source of most studies is English Wikipedia. Thus, it would be more meaningful to include more subjects from English-speaking countries and close countries. If Wikipedia subjects from all languages are united, then the result could be fairer (needs investigation).
correctness: Besides the previously mentioned points:

- This sentence from Introduction needs a source or argument: "Progress in machine learning depends on the availability of high-quality benchmark datasets, yet there is a shortage of such datasets for natural language generation"
clarity: OK.
relation to prior work: OK.
additional feedback: The notes are given humbly to improve the work. With its primary goal, it can be improved into a strong and influential study. I suggest changing the case study to a less complicated one where you can accurately analyze the impact of your work.
review_title: The subject requires more coherent and in-depth research

Meta-review: The reviewers agreed that this is an interesting idea that was clearly presented. There were a number of minor concerns from the reviewers regarding how AI-human collaboration affects dataset bias, but these were clarified in the discussion. Exciting and promising direction. I recommend it is accepted.
Decision: Accept