Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing

Q2: Novelty of the paper

We appreciate your comment and would like to clarify an important aspect:

• High-Quality Dataset: To the best of our knowledge, our work is the first to generate a high-quality open-source dataset capable of fine-tuning the Llama 3 base model to perform comparably to the official Llama Instruct version. The other datasets we compared against have not been able to achieve similar performance.

• Minimal Licensing Restrictions: Our method and dataset have minimal licensing restrictions. Based on the latest version of Llama 3's terms of use, as well as similar models like Gemma 2, the generated data from such models have a free license, making it an ideal resource for scientific research and broader applications.

• Facilitating Alignment Research: With our method and dataset, it is significantly easier to deeply study the alignment process, including conducting attribution studies (e.g., influence functions). Our goal is to make LLM alignment research more affordable and accessible by providing a truly open-source alignment recipe that is research-friendly. This approach offers high-quality data without license or user consent issues, allowing researchers to freely experiment, reproduce results, and adapt the alignment process to their needs. By making the entire dataset and methodology transparent, we aim to foster a collaborative research environment where alignment techniques can be more rigorously studied, shared, and improved upon by the broader community.

• Customization and Multimodal Potential: We find that our self-synthesize method is important for allowing users to easily customize their own data (e.g., by changing the system prompt) to generate domain-specific instructions or language-specific data without the need for any seed data. In contrast, other methods often require a set of seed data for each domain or topic, or a hand-crafted set of rules. Additionally, we believe this method is potentially helpful for generating multimodal versions of the data as well. 

We will further emphasize these contributions in the revised version of our paper.

Thank you 

Thank you again for your thoughtful review and for highlighting these points. We are confident that these clarifications will improve the understanding and presentation of our work. We will revise our paper according to your suggestions. If any concerns remain, we would appreciate further clarification and are happy to continue the discussion. We also hope our explanations encourage you to consider raising the rating of our work.

Thank you for your constructive review. We appreciate your insights and have addressed your concerns below. We will incorporate these clarifications and related discussions into the new version. We are also glad to hear that you appreciate our paper for its contribution to the open-source community, the interesting findings on synthetic user instruction generation, and the detailed insights on generated SFT and alignment pairs.

Q1: How can aligned LLMs produce user instructions?

Thank you for raising this point. We hypothesize several possible reasons for why MAGPIE can effectively generate user instructions by simply inputting the pre-query template.

First, although the learning is based on the loss of the output tokens rather than the input tokens, the token embeddings are shared and backpropagated during training. The hidden representations of the output tokens are highly contextualized by the input instructions. As a result, even though we are optimizing the model by minimizing the loss on predicting the response tokens, the token embeddings and network weights are also optimized to encode the instructions, enabling the model to better understand the query and solve tasks effectively. The learning process is unified and cannot be separated in a decoder-only architecture. Therefore, optimizing the prediction of the next tokens in the output also allows the model to implicitly memorize the instructions.

Second, the auto-regressive nature of these models allows them to complete instruction sequences effectively. Once a pre-query template is provided, the model is able to generate the remaining parts of the sequence, which includes plausible user instructions. The model's ability to complete sequences in a meaningful way stems from its extensive training on diverse datasets, which provides it with a broad understanding of various tasks and user queries.

Third, we believe that aligned LLMs leverage their training on aligned data to internalize the roles of both the user and the assistant. This means that when prompted with a pre-query template, the model can adopt the user's perspective and generate instructions that match the expected pattern. This capability allows the model to seamlessly produce user instructions without explicit training on user-side prompts, relying instead on its learned understanding of user-assistant interactions.

We agree that more theoretical work is needed to fully understand such data reconstruction phenomena. In this paper, however, our focus is on using this observation to build an open-source and transparent alignment recipe for open, scientific research, aimed at making LLM alignment research more affordable by providing high-quality data without license and user consent issues. We will add this point to further clarify our contribution.

(continued)

Dear Reviewer,

Thank you for your time and effort in reviewing our paper. Your constructive suggestions and questions have been invaluable in improving our work.

We believe our rebuttal has sufficiently addressed the concerns you raised. As the discussion period draws to a close, please let us know if you have any further questions; we would be happy to continue the discussion.

If our clarifications and responses have resolved your concerns, we kindly ask you to consider adjusting your scores accordingly.

Thank you very much for your consideration.

Best regards,

Authors

Thank you for your constructive review. We appreciate your insights and have addressed your concerns below. We will incorporate these clarifications and related discussions into the new version.

1. Magpie-MT and performance of Magpie on multiturn benchmarks.

We appreciate the reviewer's question. As described in Section 2.2, Magpie supports generating conversations with more than two turns using the num_turns parameter in our codebase (supplementary material), allowing users to extend dialogue length as needed.

Our decision to limit conversations to two turns was driven by cost-effectiveness considerations rather than technical limitations. During experiments, we found that increasing turns beyond two provided only marginal improvements on benchmarks like Alpaca Eval 2 and Arena Hard, while significantly increasing computational costs for both data generation and model training. Notably, as shown in Tables 1 and 10, Magpie's two-turn conversations already outperform multi-turn conversations from established datasets like UltraChat and WildChat.

To further validate Magpie's effectiveness in multi-turn scenarios, we conducted additional experiments using the MT-Bench benchmark. Our results, using greedy decoding, reveal that Magpie surpasses other baselines in MT-Bench evaluations. Moreover, the MT version of Magpie significantly improves Round 2 scores, demonstrating its capability to synthesize high-quality multi-turn alignment data effectively.

These results highlight that Magpie is competitive even in multi-turn settings, effectively closing the gap with more conversational datasets.

2. Limited improvements on OpenLLM leaderboard in Table 3 compared to Table 2.

We appreciate the reviewer’s question. In Table 2, we evaluate our model's alignment using benchmarks focused on instruction-following capabilities, while Table 3 measures performance on downstream tasks, such as reasoning. As discussed in Section 4.2, the relatively smaller improvements on Table 3 can be attributed to the limited proportion of reasoning instructions in the Magpie-Air and Magpie-Pro datasets.

To address this, we developed a supplementary "reasoning booster" dataset comprising 150K specialized instructions focused on mathematics, coding, and reasoning tasks, leveraging Magpie's extensibility as described in Section 2.2. By fine-tuning a model using a combination of Magpie-Pro and this booster dataset, we achieved a top-3 ranking among all model checkpoints, demonstrating Magpie's adaptability to generate task-specific instruction data for diverse performance needs.

3. Contamination analysis

We appreciate the reviewer highlighting the importance of contamination analysis. We conducted an analysis of Magpie-generated instructions using the Alpaca Eval 2 and Arena Hard benchmarks. Following [1], we used an embedding model to convert both Magpie instructions and benchmark questions into embeddings, calculating cosine similarity scores to assess potential overlap.

Our analysis reveals that Arena Hard shows no evidence of data contamination, while some similarities were found between Alpaca Eval 2 and Magpie datasets generated by Llama-3, with a few entries showing cosine similarity greater than 0.9. Notably, the affected questions account for at most 10 out of 805 benchmark questions. This is expected, as Alpaca Eval data is constructed from a mixture of existing instruction datasets, many of which contain common questions such as "What's the capital of Australia?", "Can you code?", and "What's your name?" Even with this small degree of overlap, Magpie consistently outperformed the baselines, demonstrating its robustness and showing no significant impact on benchmark evaluations. 

Thanks again and we'll include this in the final version.

(to be continued)

[1] https://arxiv.org/abs/2312.14187

4. Controllability and ablation analysis of system prompts

We understand the reviewer's comment regarding the lack of control in the generation process. However, this is a common scenario in synthetic data generation when aiming for high diversity by avoiding seed data. A common practice is to post-process the data by tagging topics and difficulties, as we have done in the paper. This approach is also discussed in Llama-3's technical report. 

Thus, we believe it is not accurate to label this as a major weakness of our method, as we have provided two main approaches to ensure diversity: 1) automatic tagging, and 2) system prompt customization, both of which have proven to be highly effective. For instance, system prompts can be used to control topics, difficulty, style, domain, and even language during synthetic data generation. This approach represents a significant improvement over existing synthetic data generation methods that rely on seed data or require human intervention to customize data diversity.  

More ablation studies on the system prompt. 

To better understand the effectiveness of system prompts, we conducted an ablation analysis focused on generating code and mathematics instructions. The results show that system prompts are highly effective in controlling the distribution of generated tasks, significantly increasing the desired task proportions. For instance, using system prompts resulted in an increase in code instruction generation from approximately 3% to over 95% of the dataset. We also found that domain-specific data, such as biomedical or legal, can be generated by simply using the same template and replacing keywords in the system prompt. This suggests a potential future direction where Magpie can be employed alongside an existing taxonomy of topics to scale data generation. This analysis demonstrates that system prompts provide a practical and efficient mechanism to guide data generation, resulting in a diverse dataset that aligns closely with specific target domains.

5. Diversity evaluation

We further evaluated the diversity of generated instructions using the Topic Diversity metric from UltraChat [2]. Our analysis in Appendix D.1 (revision) showed that Magpie-generated instructions exhibit greater diversity compared to other baselines, as demonstrated by a lower topic diversity score. Moreover, we included additional visualizations (e.g., UMAP in Figure 8 in revision) to corroborate the conclusions drawn from the T-SNE analysis in Figure 7. These analyses consistently indicate that Magpie effectively covers a wide spectrum of instruction topics.

[2] Enhancing Chat Language Models by Scaling High-quality Instructional Conversations

6. Applicability to domain-specific and multilingual datasets and performance evaluation

We appreciate the reviewer’s interest in the broader applicability of Magpie. We evaluated Magpie's applicability to domain-specific and multilingual data by generating a code-specific dataset and a Chinese-language dataset, and presented results in Appendix F.3 in revision. The experimental results demonstrate that Magpie-generated datasets improve performance on domain-specific and multilingual benchmarks compared to baseline models, highlighting the flexibility and robustness of the Magpie framework for generating high-quality, task-specific, and multilingual alignment data.

Thank you!

We appreciate your thoughtful feedback and the opportunity to address your concerns. We will revise our paper according to your suggestions and comments. We hope that our responses have clarified the points raised and provided the necessary context to understand our approach. If any concerns remain, we would be grateful for further clarification and are more than happy to continue the discussion in this rebuttal process. Additionally, we hope that our explanations will encourage you to consider raising the rating and improving the final scores of our work.

Thanks for the detailed rebuttal! My concerns regarding diversity, contamination controlling data generation using the system prompt has been addressed via results in Appendix. The results on Human Eval demonstrates good reasoning skills and on MT bench also demonstartes the multi-turn nature of the dataset. For point 1 what is the base model used? (I guess Llama-8B). I will increase my score from 6 to 8.

Thank you for your constructive review. We appreciate your insights and have addressed your concerns below. We will incorporate these clarifications and related discussions into the new version.

Concern 1: Content organization between main body and appendix

The presentation can be improved by putting more experimental results in the main context instead of the appendix.

We will reorganize the paper as suggested in the final version.

Concern 2: Qualitative comparison results

Thank you for your comments. We have provided examples of the actual generations requested by the reviewer, which can be found in Appendix H in the revised submission.

Concern 3: Benchmarking with IFEval

We have added IFEval to our evaluation. We compare the models fine-tuned with Magpie against baselines on IFEval using the LM-Evaluation-Harness framework [1]. Our results demonstrate that Magpie-generated datasets achieve comparable prompt-level and instruction-level strict accuracy scores to existing baseline datasets. Moreover, Magpie exhibits significantly higher performance in both prompt-level and instruction-level loose accuracy metrics. These findings indicate the high quality of Magpie-generated datasets. The results are presented below:

[1] Gao et al., A framework for few-shot language model evaluation, 2024

We have updated the results of IFEval in Appendix F.8 in the revised pdf.

Concern 4: How response generator affects performance

The reviewer is correct in noting that the final performance is affected by the choice of response generators for generating synthetic datasets. To address this, we conducted extensive ablation analysis using different response generators across multiple model families, as outlined below where we used three different base models and explored the influence of choosing different data generation models.

The performance correlation between the base model and the data generator model is a complex question, and here we focus on the empirical analysis as shown below. In our submission, the better performance of using Magpie-Llama data on Llama models compared to using them on the Qwen 1.5 model is mainly due to the pre-training performance being worse on Qwen. However, our data is still universally better than other datasets. Now, with more experimental results using the newer version of Qwen and additional pairs of base and generator models, we find that it is even clearer that Magpie's high-quality data is universally helpful—for example, the data generated by Qwen-2.5-72B.

While this presents an interesting area for future research, we will include these detailed findings in the final version of the paper. Our current focus is on developing scalable methods for synthesizing diverse and high-quality instructions for LLMs in this paper.

(to be continued)

Dear Reviewer,

Thank you for your time and effort in reviewing our paper. Your constructive suggestions and questions have been invaluable in improving our work.

We believe our rebuttal has sufficiently addressed the concerns you raised. As the discussion period draws to a close, please let us know if you have any further questions; we would be happy to continue the discussion.

If our clarifications and responses have resolved your concerns, we kindly ask you to consider adjusting your scores accordingly.

Thank you very much for your consideration.

Best regards,

Authors