Summary of Evolutionary Contrastive Distillation For Language Model Alignment, by Julian Katz-samuels et al.
Evolutionary Contrastive Distillation for Language Model Alignment
by Julian Katz-Samuels, Zheng Li, Hyokun Yun, Priyanka Nigam, Yi Xu, Vaclav Petricek, Bing Yin, Trishul Chilimbi
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes Evolutionary Contrastive Distillation (ECD), a novel method to enhance large language models’ ability to follow complex instructions. Recent studies have shown that LLMs struggle with challenging instructions, which hinders their real-world applications. ECD generates high-quality synthetic preference data by prompting LLMs to progressively evolve simple instructions into more complex ones. By pairing successful responses with “hard negative” responses that almost meet requirements but miss one or two, contrastive learning algorithms like DPO improve language models’ performance in following complex instructions. The proposed method achieves state-of-the-art (SOTA) results for a 7B model and is competitive even with open-source 70B models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine if computers could understand and follow complicated instructions just like humans do. This paper helps make that happen by creating a new way to teach language models to be better at understanding complex commands. Language models have trouble with tricky instructions, which is a problem because they’re supposed to help us with tasks like writing and answering questions. The new method, called Evolutionary Contrastive Distillation, makes the language models learn from examples that show what’s right and wrong. This helps them get better at following complex instructions. The result is impressive: it beats the current best models and is almost as good as even bigger models! |
Keywords
» Artificial intelligence » Distillation » Prompting
