Summary of Understanding the Relationship Between Prompts and Response Uncertainty in Large Language Models, by Ze Yu Zhang et al.
Understanding the Relationship between Prompts and Response Uncertainty in Large Language Models
by Ze Yu Zhang, Arun Verma, Finale Doshi-Velez, Bryan Kian Hsiang Low
First submitted to arxiv on: 20 Jul 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 investigates how large language models (LLMs) reason and make decisions, particularly in critical tasks like healthcare, where reliability is crucial for safe deployment. It proposes a prompt-response concept model that explains how LLMs generate responses and understands the relationship between prompts and response uncertainty. The study shows that uncertainty decreases as the prompt’s informativeness increases, similar to epistemic uncertainty. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper studies how language models work and makes decisions. It wants to know why these models can be used in important places like hospitals. To do this, it creates a new model that explains how language models respond to questions and how much they are sure about their answers. The study finds that the more information you give the model, the less unsure it is about its answer. |
Keywords
* Artificial intelligence * Prompt
