Summary of Improving Structural Diversity Of Blackbox Llms Via Chain-of-specification Prompting, by Halley Young et al.
Improving Structural Diversity of Blackbox LLMs via Chain-of-Specification Prompting
by Halley Young, Yimeng Zeng, Jacob Gardner, Osbert Bastani
First submitted to arxiv on: 12 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 proposed paper tackles a crucial challenge in large language models (LLMs), specifically generating diverse text. Currently, diversity is measured through metrics like n-gram diversity or BERT embeddings’ diversity. However, these approaches offer limited control over the dimensions of diversity consideration. For instance, in poetry, users might desire diversity in rhyme and meter, while in code, they might want diversity in expression types used to solve a problem. To address this, the authors introduce structural diversity, where users provide a mapping from generated text to features capturing desired diversity aspects. Additionally, they propose the chain-of-specification (CoS) prompting strategy for improving diversity by first generating a specification encoding one instance of structural features and then prompting the LLM to generate text satisfying these features; this strategy works with blackbox LLMs. The experiments demonstrate that CoS significantly improves diversity in poetry and code domains compared to several baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making sure language models can create many different types of text, like poems or code. Right now, we measure how diverse the text is by looking at things like word order or what words are used. But this doesn’t give us much control over what makes one piece of text more diverse than another. For example, in poetry, we might want the model to come up with different rhyming schemes and meters, while in code, we might want it to use different types of expressions to solve a problem. To fix this, the authors came up with a new way to measure diversity called structural diversity. This lets users tell the model what kind of diversity they’re looking for and get more creative text as a result. They also developed a new way to prompt the model to be more diverse by first giving it a set of guidelines and then asking it to generate text that follows those rules. The results show that this new approach works really well in both poetry and code. |
Keywords
» Artificial intelligence » Bert » Prompt » Prompting
