Summary of Xprompt:explaining Large Language Model’s Generation Via Joint Prompt Attribution, by Yurui Chang et al.
XPrompt:Explaining Large Language Model’s Generation via Joint Prompt Attribution
by Yurui Chang, Bochuan Cao, Yujia Wang, Jinghui Chen, Lu Lin
First submitted to arxiv on: 30 May 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 This study explores the relationship between input prompts and generated text in Large Language Models (LLMs). While LLMs excel in complex text generation tasks, the mechanisms behind their output remain unclear. The research proposes a novel approach, XPrompt, to explain how multiple prompt texts collectively influence the model’s complete generation. By framing prompt attribution as a combinatorial optimization problem and introducing a probabilistic algorithm, the framework aims to identify the causal input combination in the discrete space. Evaluation metrics demonstrate both faithfulness and efficiency of the proposed method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study looks at how Large Language Models (LLMs) work. It’s hard to understand why they make certain choices when generating text. The researchers developed a new way, called XPrompt, to figure out how different prompt texts affect the final result. They turned the problem into a special math puzzle and created an algorithm to solve it. This helps us understand what prompts are responsible for making LLMs generate specific text. |
Keywords
» Artificial intelligence » Optimization » Prompt » Text generation
