LLM2LLM: Boosting LLMs with Novel Iterative Data Enhancement (AI summary)

Key Points

1. The paper introduces LLM2LLM, a targeted and iterative data augmentation strategy that uses a teacher large language model (LLM) to enhance a small seed dataset by augmenting additional data. This strategy involves fine-tuning a baseline student LLM on the initial seed data, evaluating and extracting incorrect data points, and using a teacher LLM to generate synthetic data based on these incorrect data points, which are then added back into the training data.

2. LLM2LLM significantly enhances the performance of LLMs in the low-data regime, outperforming traditional fine-tuning and other data augmentation baselines. The approach reduces the dependence on labor-intensive data curation and paves the way for more scalable and performant LLM solutions, allowing for the tackling of data-constrained domains and tasks.

3. LLM2LLM achieves substantial improvements in performance, with up to 24.2% improvement on the GSM8K dataset, 32.6% on CaseHOLD, 32.0% on SNIPS, 52.6% on TREC, and 39.8% on SST-2 over regular fine-tuning in the low-data regime using a LLaMA2-7B student model.

4. Pretrained large language models are currently state-of-the-art for natural language processing tasks but may require fine-tuning in low-data regimes, which can be challenging. Traditional data augmentation methods are not effective for fine-tuning LLMs in new and specialized tasks.

5. LLM2LLM differs from existing methods as it uses the teacher model to augment data points that the student model gets incorrect during training. The paper emphasizes the importance of the iterative and targeted nature of LLM2LLM in improving model performance.

6. The paper conducts ablation studies to justify design decisions in LLM2LLM, demonstrating the effectiveness of the iterative augmentation approach as well as the use of seed data for further augmentation.

7. The paper evaluates LLM2LLM's performance on various datasets, demonstrating that the method helps improve performance in the low-data regime across different datasets.

8. LLM2LLM outperforms other augmentation techniques such as EDA and AugGPT, indicating its capability to generate more targeted examples based on where the model struggles, as opposed to indiscriminate data augmentation.

9. The paper provides detailed experimental results and analysis, highlighting the substantial impact of LLM2LLM in improving LLM performance with small seed datasets, and discusses potential future work and considerations for integrating LLM2LLM with other LLM techniques.

Summary

The paper proposes a targeted and iterative data augmentation strategy, LLM2LLM, designed to boost the performance of large language models (LLMs) in low-data regimes. The approach involves fine-tuning a baseline student LLM on initial seed data, evaluating and extracting incorrect data points, and then using a teacher LLM to generate synthetic data for reintegration into the training data. The paper demonstrates significant performance enhancements in low-data scenarios, surpassing traditional fine-tuning and other data augmentation methods on various datasets.

Application of Large Language Models
Pretrained large language models are currently state-of-the-art for solving natural language processing tasks. However, real-world applications often require fine-tuning in low-data scenarios, making it challenging to achieve satisfactory performance. In response, LLM2LLM proposes an iterative approach that uses a teacher LLM to enhance a small seed dataset by generating additional data for fine-tuning. The approach fine-tunes a baseline student LLM on the initial seed data, evaluates and extracts data points that the model gets wrong, and uses a teacher LLM to generate synthetic data based on these incorrect data points, which are then added back into the training data. The paper reports significant performance improvements in the low-data regime, achieving up to 24.2% improvement on the GSM8K dataset, 32.6% on CaseHOLD, 32.0% on SNIPS, 52.6% on TREC, and 39.8% on SST-2 over regular fine-tuning using a LLaMA2-7B student model.

Effectiveness Validation of LLM2LLM
The paper conducts ablation studies to evaluate the effectiveness of the design decisions and highlights the superiority of the iterative and targeted nature of LLM2LLM in improving model performance. Furthermore, the paper compares LLM2LLM with other data augmentation methods such as EDA and AugGPT, demonstrating LLM2LLM's capability to generate more targeted examples based on where the model struggles, resulting in more effective use of the augmented data budget. The authors also conduct experiments to evaluate the performance of LLM2LLM with different teacher LLMs, showing the impact of the teacher model on the quality of augmentation and, consequently, the accuracy of LLM2LLM.

To conclude, the paper introduces a novel data augmentation framework, LLM2LLM, which efficiently and effectively augments small task-specific datasets, reducing the need for labor-intensive data curation and paving the way for more scalable and performant LLM solutions in data-constrained domains and tasks. The authors suggest future work could focus on tuning the hyperparameters of the framework and incorporating it with other LLM techniques such as prompt tuning and few-shot learning.

Reference: https://arxiv.org/abs/2403.150...