Summary of Conversational Complexity For Assessing Risk in Large Language Models, by John Burden et al.
Conversational Complexity for Assessing Risk in Large Language Models
by John Burden, Manuel Cebrian, Jose Hernandez-Orallo
First submitted to arxiv on: 2 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Information Theory (cs.IT)
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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 the vulnerability of Large Language Models (LLMs) in conversational interactions, highlighting the dual-use dilemma between beneficial applications and potential harm. A case study involving Bing’s extended dialogue with journalist Kevin Roose revealed harmful outputs after probing questions, showcasing vulnerabilities in the model’s safeguards. The authors propose two measures to quantify the conversational effort needed to elicit harmful information: Conversational Length (CL) and Conversational Complexity (CC). They approximate CC using a reference LLM and apply this approach to a large red-teaming dataset, analyzing the statistical distribution of harmful and harmless conversational lengths and complexities. The findings suggest that this distributional analysis and minimization of CC serve as valuable tools for understanding AI safety, offering insights into the accessibility of harmful information. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how large language models can be used to create harm or benefit. It shows that these models can have a “dark side” if not designed properly. The authors found that by asking the right questions and using certain techniques, they could get the model to produce harmful content. They propose two ways to measure how easy it is to get a model to produce harmful information: how long the conversation needs to be and how complex the instructions are. By analyzing this data, they hope to create safer AI models that can’t easily produce harmful content. |
