Paper: Achieving Human Parity in Content-Grounded Datasets Generation (ICLR-2024)

Reviewer comment (Experiments):
Content-grounding generation is a broad topic, and the paper only mentions long-form QA and summarization. However, if we look at it purely from the perspective of "Content-grounding generation," most traditional NLP tasks can be transformed into the paradigm: Instruction+Input -> Output, which matches with the definition of Content-grounding generation. However, the paper only validates this on two tasks. Furthermore, in subsequent experiments, the paper only constructs data based on specific datasets, raising doubts about the generality of the proposed method. Therefore, I tend to believe that the paper exaggerates its contributions.

Author response:
Thank you very much for your helpful review. We are glad you find our method innovative and practical, and our filtering to be a systematic approach ensuring the quality and faithfulness of the data. We also appreciate you find the experimental comparison of models trained on our synthetic data with models trained on human-generated data to be insightful and empirically sound.

1. We completely agree that Content-grounding generation is a broad topic. We chose two permanent tasks, LFQA, and summarization, as their representative. For instance, the Instruction data for the Dolly model consisted of three content-grounded tasks, two of which are LFQA and summarization (https://www.databricks.com/blog/2023/04/12/dolly-first-open-commercially-viable-instruction-tuned-llm ). The third task, information extraction, is highly related to LFQA and can be viewed as an extractive version of LFQA. We demonstrated that our method is robust to different setups for the LFQA task, including multi-document and single-document, data collected from scratch and data from existing datasets, data from Wikipedia and data from the medical domain, and additional practical cases (which we could not report due to data privacy). Moreover, we showed that our method is also robust for different generation models and trained models. Consequently, we see no reason why our method would not hold for other content-grounded tasks like information extraction or paraphrasing. Additionally, to clear this concern, we plan to demonstrate that our method remains effective for additional tasks and other datasets to establish its generality beyond the initial two tasks.

Reviewer comment (Experiments):
The data construction methods mentioned in the paper rely on the format and source (Wiki) of previous datasets, making it challenging to assess their practicality in an open-world context.

Author response:
Regarding the claim that our data construction method is limited, as we explained in the paper, we have a general approach for content preparation that uses a set of pages and prepares them at a passage level, ready for the two subsequent steps in the data generation process. We demonstrated this setup in the creation of the WiFS dataset (Wikipedia From Scratch). This step is general, and we applied it to several domains, including banking, mobile telecom, sports, and IT support (practical cases where the data is private). Regarding the use of previous datasets, we used them mostly to facilitate fair comparison. We are not limited to Wikipedia, as we also used PubMed and CNN-DailyMail. In most cases, those datasets were processed automatically. Google-NQ is the only dataset where human annotators were involved in extracting the passage from Wikipedia, but our setup did not require the use of questions, making the contribution of human annotators less crucial. Furthermore, the model trained on the synthetic WiFS data performed equally or better in terms of quality and faithfulness than the one trained on Google-NQ, indicating that the Google-NQ content preparation setup is not crucial for the success of our method. We will make this point clearer in the final version.

Reviewer comment (Evaluation):
The paper claims that high noise levels exist in existing datasets, such as news domains. This claim necessitates empirical validation. Additionally, the test set originates from the same noisy source. It is necessary to perform a Multi-Dimensional Quality assessment on both the original and synthetic datasets to evaluate genuine quality enhancements. The paper lacks specifics on annotation details, including inter-annotator agreements.

Author response:
1. Regarding providing support for the claim that existing datasets have high noise levels. We state that the process used to collect a large amount of content-grounded data is noisy as it relies on some assumption, e.g. the answer/summary is based on the documents, and the answer adequately addresses the question. This was found to be a main problem for both LFQA and summarization. For LFQA, https://aclanthology.org/2021.naacl-main.393/ found that large numbers of  ELI5 answers are not faithful to the documents and described it as one hurdle for advancing the task of LFQA. In summarization, https://aclanthology.org/2023.findings-acl.220/ describes that “models still suffer from generating factually inconsistent summaries, reducing their utility for real-world application” in regard to the XLSum news summarization dataset. We can add that this is not always the case as the CNN-DailyMail dataset seems to be very faithful by reviewing the faithfulness scores of models trained on it. Accordingly, We base our claim that datasets created with this process can be noisy on the conclusions of the cited papers. We will make sure it is clearer in the paper's final version. 

Regarding the test being noisy, you are right that there is a problem with the assessment on those test sets. First, we note that for the LFQA test set, this problem is relevant for the ELI5 test, while the ASQA was human writing and the PubMed was humanly validated alleviating the concern of them being noisy. Therefore, when seeing that the different test sets share similar trends (for example, the best-performing model is mostly the same; Tables 2 & 3) we get an additional indication of the validity of our results. For the summarization test set, the CNN-DailyMail seems mostly faithful making this concern less severe. We will add this clarification to the paper.

Regarding annotation details, two expert annotators participated in the study. In the Naturalness Evaluation experiments, the maximum discrepancy between their assessments was 6%. The inter-annotator agreements for ELI5, Google-nq, and ASQA evaluations are 70% (Cohen's Kappa: 0.40), 64% (Kappa: 0.28), and 57% (Kappa: 0.14), respectively. These figures are indicative of the varying degrees of difficulty in distinguishing between human and model writing across different datasets. In the Multi-Dimensional Quality assessment, the greatest difference observed per question annotation was 7%, and the average overall quality difference was 0.17. There was a slightly higher variation for WiFS w/o filters and a lower difference for Synthetic NQ. We will also add this to the paper.

Reviewer comment (Novelty):
The paper is more like an empirical exploration into using LLMs for synthetic data generation, which is not inspiring for other works.

Author response:
3. You are surely entitled to this opinion (is such work against the guidelines?), however, we do not share it. Synthetic data generation is an important field of research that showed promising results in improving model abilities. From that perspective, our work shows a novel method to create high-quality, content-grounded data which is cost and time-efficient improves over strong basslines, and shows that our data achieves human level which was not shown before. Other than that, the data itself has been a bottleneck to developing content-grounded models, and our opinion is hence that the released data and method would enable such work in other domains that lack this data.

Reviewer comment (Novelty):
While I have not noticed any technical issues with the paper, the main weakness seems to be the novelty and the scope of the contribution.

Author response:
Thank you very much for your helpful review. We are glad you find our paper well-written, our method effective in producing human-level data, our evaluations to be thorough, and our paper to be technically sound.

Regarding the scope of our contribution, we agree that the 2-step-based generation is indeed an intuitive, effective, and broad approach with many different realizations for different tasks. Our work utilizes this approach in an innovative way,  as highlighted by R3,  to address a real-world challenge identified in previous works as well as practical applications.

Our method tackles the real-world problem of limited faithful content-grounded data which hinders the usability of trained models in real-world applications. In response, we present an efficient method to generate such data, providing a general approach for creating such data from any given content.  Our results demonstrate that models trained on our data not only match but often surpass the performance of models trained on human data, with a larger advantage in terms of faithfulness.

High-quality content-grounded data is scarce (https://arxiv.org/abs/2204.06092) as large datasets are limited in their faithfulness (https://aclanthology.org/2021.naacl-main.393/). This scarcity calls for ways to create such datasets. 
Previous works for content-grounded data generation either created a large dataset with limited naturalness (dialogue inpainting) or paid a lot to annotators to create a fairly small (6k) dataset (e.g., ASQA).

Additionally, most previous methods are not fitting to generate knowledge-based faithful data. Most methods rely on the LLMs' parametric knowledge as a source of information without verifying its validity.

 In contrast, our method enables the creation of natural and faithful task-specific data grounded in specific content, offering a solution for tasks and domains with limited data availability. Moreover, it paves the way for comprehensive pipelines that span from scraped data to task-specific datasets, showcasing significant potential.

It's worth noting that our method was specifically designed to address a problem we encountered, emphasizing its practical relevance and targeted efficacy. We appreciate your feedback and will make sure that our significant contribution to advancing the field of content-grounded data generation will be clearer.

Reviewer comment (Novelty):
The 2-step based generation seems fairly obvious as an approach, and it seems already been tried before in a few works as elaborated in the related works.

Author response:
Thank you very much for acknowledging our contribution to the LFQA community and deciding to increase the score.
We are still a bit unsure about what you consider similar techniques. As far as we know, no prior works on synthetic data generation have used either reward models or models for faithfulness detection. These two approaches have been shown to be effective in improving the quality of our synthetic content-grounded data. Moreover, most previous works heavily rely on instruct-tuned proprietary models, limiting their usability.
Regarding the suggestion of reviewer pq6E for a multidimensional comparison of ELI5, ASQA, human, and synthetic data, we conducted this experiment with the same setup as described in the paper. Here are the results, which we will add to the paper:


| Quality Review Question                           | ELI5  | ASQA  | ELI5 - synthetic | ASQA - synthetic |
|---------------------------------------------------|-------|-------|------------|------------|
| Is the question relevant to the content?           | 77%   | 100%  | 100%       | 99%        |
| Is the question open-ended? (not ambiguous)             | 31%   | 23%   | 57%        | 54%        |
| Does the answer address the question?              | 92%   | 96%   | 100%       | 98%        |
| Is the answer faithful to the content?             | 15%   | 72%   | 95%        | 92%        |
| Grad the quality of the answer (1-5)               | 3.98  | 4.88  | 4.74       | 4.73       |

In the table, you can see that except for ELI5, the majority of questions were relevant to the content. Indeed, many questions are non-factoid questions with multiple possible answers. In all datasets, the answers address the questions in most cases. Regarding the faithfulness of the answers to the content, ELI5 is far behind with only 15% of cases being faithful to the content, while ASQA is much better in that aspect but still not as faithful as our synthetic data. These results are in line with our expectations, such as the faithfulness automatic metrics results of the fine-tuned models and the ASQA paper manual analysis (if taking into account our use of top-3 passages), which suggest a similar discrepancy in dataset quality. Finally, the overall answer quality of ASQA appears to be the best, slightly higher than the synthetic data scores. Both significantly outperform the ELI5 scores.
Regarding the inter-annotator agreement scores, here is a table with their scores:

| Naturalness Evaluation Experiment | Maximum Discrepancy | Inter-Annotator Agreement (%) | Cohen's Kappa |
|------------------------------------------|---------------------|-------------------------------|---------------|
| ELI5             | 5%                  | 70%                           | 0.40          |
| Google-nq        | 6%                  | 64%                           | 0.28          |
| ASQA             | 6%                  | 57%                           | 0.14          |

Regarding the additional relevant papers about query-focused summarization and non-factoid QA, they indeed seem relevant, and we will add them. We will also look for more works on those topics that are relevant.
Regarding the release of the dataset, as we mentioned in the paper (just before Section 2), we plan to publicly release the data.

Reviewer comment (Novelty):
I am open to increasing the score if I am missing some context, highlights, or critical contrast compared to prior works. This could be provided in the rebuttal.

Author response:
Thank you very much for your helpful review. We are glad you find our paper well-written, our method effective in producing human-level data, our evaluations to be thorough, and our paper to be technically sound.

Regarding the scope of our contribution, we agree that the 2-step-based generation is indeed an intuitive, effective, and broad approach with many different realizations for different tasks. Our work utilizes this approach in an innovative way,  as highlighted by R3,  to address a real-world challenge identified in previous works as well as practical applications.

Our method tackles the real-world problem of limited faithful content-grounded data which hinders the usability of trained models in real-world applications. In response, we present an efficient method to generate such data, providing a general approach for creating such data from any given content.  Our results demonstrate that models trained on our data not only match but often surpass the performance of models trained on human data, with a larger advantage in terms of faithfulness.

High-quality content-grounded data is scarce (https://arxiv.org/abs/2204.06092) as large datasets are limited in their faithfulness (https://aclanthology.org/2021.naacl-main.393/). This scarcity calls for ways to create such datasets. 
Previous works for content-grounded data generation either created a large dataset with limited naturalness (dialogue inpainting) or paid a lot to annotators to create a fairly small (6k) dataset (e.g., ASQA).

Additionally, most previous methods are not fitting to generate knowledge-based faithful data. Most methods rely on the LLMs' parametric knowledge as a source of information without verifying its validity.

 In contrast, our method enables the creation of natural and faithful task-specific data grounded in specific content, offering a solution for tasks and domains with limited data availability. Moreover, it paves the way for comprehensive pipelines that span from scraped data to task-specific datasets, showcasing significant potential.

It's worth noting that our method was specifically designed to address a problem we encountered, emphasizing its practical relevance and targeted efficacy. We appreciate your feedback and will make sure that our significant contribution to advancing the field of content-grounded data generation will be clearer.