Self-Alignment with Instruction Backtranslation (AI summary)

Key Points

1. Challenges of aligning large language models to perform instruction following often require finetuning on large amounts of human-annotated instructions, but collecting such high-quality annotations at scale is difficult.

2. The "instruction backtranslation" method leverages unlabelled data and develops an iterative self-training algorithm to create a high-quality instruction tuning dataset. The method uses the model to augment and curate high-quality training examples to improve its own performance.

3. The self-training approach assumes access to a base language model, a small amount of seed data, and a collection of unlabelled examples, such as a web corpus.

4. The proposed method, Humpback, outperforms all other existing non-distilled models on the Alpaca leaderboard, demonstrating highly effective self-alignment. The model also competes with those trained on "distillation data."

5. The paper evaluates the method's performance using a range of prompts from various sources such as Vicuna, Self-instruct, Open Assistant, and others. The evaluation includes both automatic and human preference evaluations.

6. Experiment results show that increasing the quantity of high-quality data provides further gains in learning to follow instructions.

7. The method is compared to other instruction tuning datasets and models to evaluate its data scaling efficiency and improvement over the seed model.

8. The authors also discuss how the method could potentially amplify biases from web data and evaluate model responses on potentially sensitive prompts, as well as failure cases of the method.

Summary

The paper presents a method called instruction backtranslation that leverages large amounts of unlabeled data to create a high-quality instruction tuning dataset for language models. The method involves self-training the model to augment and curate high-quality training examples, resulting in improved model performance. The resulting model, named Humpback, outperforms all other existing non-distilled models on the Alpaca leaderboard. The paper provides an overview of the instruction backtranslation method, showcasing the process from using a base language model and a small amount of seed examples to self-curating high-quality training data for model improvement.

Additionally, the paper discusses related work on aligning large language models to perform instruction following and highlights the challenges and importance of data quality in instruction tuning for language models. Furthermore, the paper details experiments and evaluations conducted to demonstrate the effectiveness of the proposed method in improving language model performance, such as scaling efficiency, improvement over the seed model, and biases detection. It also describes the human evaluation process used for the experiment and summarizes the improvement over the seed model in mathematical reasoning, general information seeking, providing advice, and writing. The paper concludes with discussions on the limitations and future work of instruction backtranslation, emphasizing the potential for further scaling and improvement of the method.

Introduction of Novel Method and Model Performance
The research paper introduces a novel method called instruction backtranslation, which utilizes a self-training algorithm to leverage unlabeled data for creating a high-quality instruction tuning dataset. This algorithm involves the model itself augmenting and curating high-quality training examples to improve its own performance. The resulting model, Humpback, outperforms all existing non-distilled models. The paper outlines the instruction backtranslation method, showcasing the process from using a base language model and a small amount of seed examples to self-curating high-quality training data for model improvement. Additionally, the paper demonstrates Humpback’s superiority over other models and provides an overview of the process involved.

The study reaffirms the importance of preventing harm to animals, the need to address bullying through communication and support systems, and the significance of evidence-based claims in elections. The research paper also presents examples where Humpback fails to provide appropriate responses, underscoring the limitations of the model. Furthermore, it discusses data scaling efficiency experiments, noting the same base LLaMa model finetuned on different datasets for the same number of steps with the same batch size for each data scale N.

Reference: https://arxiv.org/abs/2308.06259