Summary of Efficient Alignment Of Large Language Models Via Data Sampling, by Amrit Khera et al.
Efficient Alignment of Large Language Models via Data Sampling
by Amrit Khera, Rajat Ghosh, Debojyoti Dutta
First submitted to arxiv on: 15 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the challenge of aligning large language models (LLMs) with human values, goals, and intentions. To achieve this, aligning LLMs require substantial computational resources, time, and data. Recent studies have shown that data engineering can improve fine-tuning and pre-training paradigms to reduce costs. However, alignment differs from these approaches, and it’s unclear if data-efficient alignment is feasible. The authors investigate how the performance of LLM alignment scales with data, finding an exponential plateau pattern that tapers off after a rapid initial increase. They identify data subsampling as a viable method to reduce resources required for alignment and propose an information theory-based methodology for efficient alignment. By identifying a small high-quality subset, they reduce computation and time requirements. Evaluating their proposed methodology across multiple datasets, they find it outperforms other sampling methods while using less than 10% of the data, resulting in over 90% savings in costs and resources. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making sure big language models behave safely and work well with human goals. It’s like trying to teach a super smart computer how to be kind and helpful. Right now, it takes a lot of time, money, and data to make these computers “safe” and aligned with what humans want. Some researchers have found ways to make this process faster and cheaper by using big datasets and powerful computers. But the problem is that this approach doesn’t always work, and we don’t know if there’s a way to make it more efficient. The authors of this paper looked at how well the “safety” training works with different amounts of data and found that it gets better really quickly but then starts to get slower. They also found a way to make the process faster by only using a small part of the data, which saves time and money. |
Keywords
» Artificial intelligence » Alignment » Fine tuning
