Summary of Are Large Language Models Really Bias-free? Jailbreak Prompts For Assessing Adversarial Robustness to Bias Elicitation, by Riccardo Cantini et al.
Are Large Language Models Really Bias-Free? Jailbreak Prompts for Assessing Adversarial Robustness to Bias Elicitation
by Riccardo Cantini, Giada Cosenza, Alessio Orsino, Domenico Talia
First submitted to arxiv on: 11 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large Language Models (LLMs) have gained significant attention for their impressive computational power and linguistic abilities. However, they are susceptible to various biases rooted in their training data. This paper investigates the presence of selection, linguistic, confirmation, and stereotype biases within LLM responses, analyzing their impact on fairness and reliability. Additionally, the study explores how prompt engineering techniques can be exploited to reveal hidden biases, testing their robustness against specially crafted “jailbreak” prompts designed for bias elicitation. The experiments demonstrate that even advanced LLMs can produce biased or inappropriate responses when manipulated, highlighting the need for enhanced mitigation techniques to address these safety concerns and promote a more inclusive AI. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models are super smart computers that can understand and generate human-like text. But did you know they have some built-in biases? These biases come from the way they were trained on data, which is often biased towards certain groups of people. This paper looks at how these biases affect what LLMs say, and how we can use special tricks to make them reveal their hidden biases. It turns out that even really smart LLMs can still be tricked into saying things that aren’t fair or nice. So, we need to find ways to fix this problem and make sure AI is safe and respectful for everyone. |
Keywords
» Artificial intelligence » Attention » Prompt
