Summary of Selfprompt: Autonomously Evaluating Llm Robustness Via Domain-constrained Knowledge Guidelines and Refined Adversarial Prompts, by Aihua Pei et al.
SelfPrompt: Autonomously Evaluating LLM Robustness via Domain-Constrained Knowledge Guidelines and Refined Adversarial Prompts
by Aihua Pei, Zehua Yang, Shunan Zhu, Ruoxi Cheng, Ju Jia
First submitted to arxiv on: 1 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 A novel framework is introduced to autonomously evaluate the robustness of large language models (LLMs) by incorporating refined adversarial prompts and domain-constrained knowledge guidelines. The method generates descriptive sentences from domain-constrained knowledge graph triplets to formulate adversarial prompts, enhancing their relevance and challenge. These prompts are then filtered and refined to ensure high textual fluency and semantic fidelity. The self-evaluation mechanism allows LLMs to assess their robustness without relying on external benchmarks. This approach reduces dependency on conventional data and provides a targeted means of evaluating LLM robustness in constrained domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to test how well language models can handle unexpected questions or tricky situations. The idea is to create special prompts that are tailored to the model’s own strengths and weaknesses, so it can evaluate its performance itself. This approach gets rid of the need for external testing data and makes it easier to check how well a model works in specific areas. |
Keywords
» Artificial intelligence » Knowledge graph
