Textbooks Are All You Need: Conclusion and References

Table of Links

• Abstract and 1. Introduction
• 2 Training details and the importance of high-quality data
• 2.1 Filtering of existing code datasets using a transformer-based classifier
• 2.2 Creation of synthetic textbook-quality datasets
• 2.3 Model architecture and training
• 3 Spikes of model capability after finetuning on CodeExercises, 3.1 Finetuning improves the model’s understanding, and 3.2 Finetuning improves the model’s ability to use external libraries
• 4 Evaluation on unconventional problems with LLM grading
• 5 Data pruning for unbiased performance evaluation
• 5.1 N-gram overlap and 5.2 Embedding and syntax-based similarity analysis
• 6 Conclusion and References
• A Additional examples for Section 3
• B Limitation of phi-1
• C Examples for Section 5

6 Conclusion

Just as a comprehensive, well-crafted textbook can provide a student with the necessary knowledge to master a new subject, our work demonstrates the remarkable impact of high-quality data in honing a language model’s proficiency in code-generation tasks. By crafting “textbook quality” data we were able to train a model that surpasses almost all open-source models on coding benchmarks such as HumanEval and MBPP despite being 10x smaller in model size and 100x smaller in dataset size. We hypothesize that such high quality data dramatically improves the learning efficiency of language models for code as they provide clear, self-contained, instructive, and balanced examples of coding concepts and skills.

There remains a number of limitations of our model compared to larger models for code. Firstly, phi-1 is specialized in Python coding, which restricts its versatility compared to multi-language models. Secondly, phi-1 lacks the domain-specific knowledge of larger models such as programming with specific APIs or using less common packages. Lastly, due to the structured nature of the datasets and the lack of diversity in terms of language and style, phi-1 is less robust to stylistic variations or errors in the prompt (for instance, its performance substantially degrades when there are grammatical mistakes in the prompt). We expand on these limitations and give examples of the failure modes of phi-1 in Appendix B.

None of these limitations seem fundamental, and with more work our approach could be used to tackle each one of them, although it is unclear what scaling might be necessary to overcome them (both for the model size and the dataset size). We also believe that significant gains could be achieved by using GPT-4 to generate the synthetic data instead of GPT-3.5, as we noticed that GPT-3.5 data has a high error rate. It is interesting that phi-1 is able to achieve such high coding proficiency despite those errors (a similar phenomenon was observed in [AZL23] where a language model can be trained on data with 100% error rate and still generate correct answers at test time).

More generally, our work provides evidence that developing good methodology for creating highquality datasets is a central direction of research for advancing natural language processing and related fields (see also [JWJ+ 23] for further evidence). However, creating high-quality datasets is not a trivial task, and it poses several challenges that need to be addressed. One challenge is to ensure that the dataset covers all the relevant content and concepts that one wants the model to learn, and that it does so in a balanced and representative way. Another challenge is to ensure that the dataset is truly diverse and non-repetitive, so that the model does not simply overfit to the data or memorize specific patterns or solutions. This requires finding ways to inject randomness and creativity into the data generation process, while still maintaining the quality and the coherence of the examples. Moreover, even after creating such datasets, we lack a good methodology to measure and evaluate the amount of diversity and redundancy in the data. For example, if we have a dataset with coding exercises, it is hard to determine how many different variations of each exercise exist, and how they are distributed across the dataset. Finally, as language models themselves will be used to curate data for future language models, it further increases the urgency on the ethical and social implications of training such models, such as the accountability, the transparency, and the bias of the data and the models that are involved in this process.

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