Summary of Llmauditor: a Framework For Auditing Large Language Models Using Human-in-the-loop, by Maryam Amirizaniani et al.
LLMAuditor: A Framework for Auditing Large Language Models Using Human-in-the-Loop
by Maryam Amirizaniani, Jihan Yao, Adrian Lavergne, Elizabeth Snell Okada, Aman Chadha, Tanya Roosta, Chirag Shah
First submitted to arxiv on: 14 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 paper proposes a solution to identify and address potential issues with Large Language Models (LLMs) such as bias, inconsistencies, and hallucination. The proposed approach, LLMAuditor, utilizes a different LLM along with human-in-the-loop (HIL) verification to create reliable and scalable probes for auditing. The framework consists of two phases: standardized evaluation criteria to verify responses and a structured prompt template to generate desired probes. A case study using the TruthfulQA dataset demonstrates the effectiveness of LLMAuditor in generating reliable probes and reducing hallucinated results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are becoming increasingly popular, but they can also be biased or inconsistent. To fix these problems, we need a way to test LLMs automatically and reliably. One approach is to ask the same question multiple times with small changes, which can reveal if an LLM is biased or inconsistent. However, creating these questions automatically is difficult. This paper proposes a solution called LLMAuditor that uses another LLM and human help to create reliable questions. |
Keywords
* Artificial intelligence * Hallucination * Prompt
