Bey Points

Easy-to-Hard Generalization in Language Model Supervision
The research paper explores the challenges of supervising language models (LMs) in specialized domains, focusing on the concept of easy-to-hard generalization. The paper presents findings that current language models often generalize relatively well from easy to hard data, performing as well as models trained on hard data. The paper explores the difficulties in labeling data in specialized domains and implications for LM training, as well as how easy supervision may outperform hard supervision in certain scenarios, particularly when it is difficult to collect and label hard data. It also investigates the impact of model scale and the gap between train and test hardness on easy-to-hard generalization. Additionally, the paper offers insights into data hardness stratification, the implications for curriculum learning, compositional generalization, and the importance of measuring easy-to-hard generalization.

Findings on Generalization and Data Hardness
The research found that LMs generalize surprisingly well from easy to hard data, closing 70%-100% of the performance gap between unsupervised-to-hard and hard-to-hard accuracy across various measures of datapoint hardness. The study also demonstrated that it may be better to train on easy data when hard data is more expensive to collect or has noisier labels. Furthermore, the research showed that as models scale up, the supervision gap recovered remains consistent, and easy-to-hard performance may begin to decline when the gap between train and test hardness becomes sufficiently large.

Conclusions and Implications
The paper concludes that training on relatively small amounts of easy data successfully elicits relevant task knowledge from models, and emphasizes the importance of using multiple notions of human hardness to assess data hardness and generalization. The research also addresses the challenges of using test questions about knowledge from beyond pretraining data cutoffs and highlights the potential implications for future work in supervising language models in specialized domains.

Summary

1. The paper addresses the issue of the "scalable oversight problem," focusing on the effectiveness of current language models (LMs) in generalizing from easy to hard data and the implications for supervised learning in specialized domains of human knowledge.

2. The study finds that current language models often generalize relatively well from easy to hard data, demonstrating this generalization using simple training methods like in-context learning, linear classifier heads, and QLoRA for several measures of datapoint hardness.

3. The difficulty in supervising LMs in specialized domains arises from the challenge of correctly labeling data in such domains, which can be time-consuming and susceptible to label noise.

4. The paper presents empirical evidence that easy-to-hard generalization in LMs is surprisingly strong for the studied tasks, suggesting that easy supervision may outperform hard supervision in certain settings.

5. The research explores the cost-benefit tradeoffs of collecting easy vs. hard training data and concludes that it may be better to collect and train on easy data given its potential to outperform hard data and its lower cost and label noise.

6. The study also investigates how easy-to-hard generalization changes with model scale and the gap between train and test hardness, finding that the Supervision Gap Recovered is highly robust across different model scales and may decline when the gap between train and test hardness becomes large.

7. The research introduces diversity in measuring data hardness through multiple human-based hardness measures as well as a model-based measure, providing empirical evidence that these measures capture different aspects of datapoint hardness.

8. The study discusses the implications of training on easy data for improving model performance in hard tasks, with specific focus on the tradeoffs and potential benefits of training on easy data.

9. The paper highlights the sample efficiency and robustness of language models trained on small amounts of easy data, revealing that such training efficiently elicits relevant knowledge from the models, particularly in invariant ways across domain and data hardness.

Reference: https://arxiv.org/abs/2401.06751