Desert Camels and Oil Sheikhs: Arab-Centric Red Teaming of Frontier LLMs

• URL: http://arxiv.org/abs/2410.24049v2
• Date: Sat, 02 Nov 2024 19:02:32 GMT
• Title: Desert Camels and Oil Sheikhs: Arab-Centric Red Teaming of Frontier LLMs
• Authors: Muhammed Saeed, Elgizouli Mohamed, Mukhtar Mohamed, Shaina Raza, Shady Shehata, Muhammad Abdul-Mageed,
• Abstract summary: Large language models (LLMs) are widely used but raise ethical concerns due to embedded social biases. This study examines LLM biases against Arabs versus Westerners across eight domains, including women's rights, terrorism, and anti-Semitism. We evaluate six LLMs -- GPT-4, GPT-4o, LlaMA 3.1 (8B & 405B), Mistral 7B, and Claude 3.5 Sonnet.
• Score: 15.432107289828194
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) are widely used but raise ethical concerns due to embedded social biases. This study examines LLM biases against Arabs versus Westerners across eight domains, including women's rights, terrorism, and anti-Semitism and assesses model resistance to perpetuating these biases. To this end, we create two datasets: one to evaluate LLM bias toward Arabs versus Westerners and another to test model safety against prompts that exaggerate negative traits ("jailbreaks"). We evaluate six LLMs -- GPT-4, GPT-4o, LlaMA 3.1 (8B & 405B), Mistral 7B, and Claude 3.5 Sonnet. We find 79% of cases displaying negative biases toward Arabs, with LlaMA 3.1-405B being the most biased. Our jailbreak tests reveal GPT-4o as the most vulnerable, despite being an optimized version, followed by LlaMA 3.1-8B and Mistral 7B. All LLMs except Claude exhibit attack success rates above 87% in three categories. We also find Claude 3.5 Sonnet the safest, but it still displays biases in seven of eight categories. Despite being an optimized version of GPT4, We find GPT-4o to be more prone to biases and jailbreaks, suggesting optimization flaws. Our findings underscore the pressing need for more robust bias mitigation strategies and strengthened security measures in LLMs.

Related papers

• Evaluating Gender Bias of LLMs in Making Morality Judgements [15.997086170275615]
  This work investigates whether current closed and open-source Large Language Models (LLMs) possess gender bias. To evaluate these models, we curate and introduce a new dataset GenMO (Gender-bias in Morality Opinions) We test models from the GPT family (GPT-3.5-turbo, GPT-3.5-turbo-instruct, GPT-4-turbo), Llama 3 and 3.1 families (8B/70B), Mistral-7B and Claude 3 families (Sonnet and Opus) All models consistently favour female characters, with GPT showing bias in 68-85% of cases and Llama 3 in around
  arXiv  Detail & Related papers  (2024-10-13T20:19:11Z)
• Are Large Language Models Strategic Decision Makers? A Study of Performance and Bias in Two-Player Non-Zero-Sum Games [56.70628673595041]
  Large Language Models (LLMs) have been increasingly used in real-world settings, yet their strategic decision-making abilities remain largely unexplored. This work investigates the performance and merits of LLMs in canonical game-theoretic two-player non-zero-sum games, Stag Hunt and Prisoner Dilemma. Our structured evaluation of GPT-3.5, GPT-4-Turbo, GPT-4o, and Llama-3-8B shows that these models, when making decisions in these games, are affected by at least one of the following systematic biases.
  arXiv  Detail & Related papers  (2024-07-05T12:30:02Z)
• Evaluating Implicit Bias in Large Language Models by Attacking From a Psychometric Perspective [66.34066553400108]
  We conduct a rigorous evaluation of Large Language Models' implicit bias towards certain groups by attacking them with carefully crafted instructions to elicit biased responses. We propose three attack approaches, i.e., Disguise, Deception, and Teaching, based on which we built evaluation datasets for four common bias types.
  arXiv  Detail & Related papers  (2024-06-20T06:42:08Z)
• White Men Lead, Black Women Help? Benchmarking Language Agency Social Biases in LLMs [58.27353205269664]
  Social biases can manifest in language agency. We introduce the novel Language Agency Bias Evaluation benchmark. We unveil language agency social biases in 3 recent Large Language Model (LLM)-generated content.
  arXiv  Detail & Related papers  (2024-04-16T12:27:54Z)
• How Far Are We on the Decision-Making of LLMs? Evaluating LLMs' Gaming Ability in Multi-Agent Environments [83.78240828340681]
  We introduce GAMA($gamma$)-Bench, a new framework for evaluating Large Language Models' Gaming Ability in Multi-Agent environments. $gamma$-Bench includes eight classical game theory scenarios and a dynamic scoring scheme specially designed to assess LLMs' performance. Results indicate GPT-3.5 demonstrates strong robustness but limited generalizability, which can be enhanced using methods like Chain-of-Thought.
  arXiv  Detail & Related papers  (2024-03-18T14:04:47Z)
• Pride and Prejudice: LLM Amplifies Self-Bias in Self-Refinement [75.7148545929689]
  Large language models (LLMs) improve their performance through self-feedback on certain tasks while degrade on others. We formally define LLM's self-bias - the tendency to favor its own generation. We analyze six LLMs on translation, constrained text generation, and mathematical reasoning tasks.
  arXiv  Detail & Related papers  (2024-02-18T03:10:39Z)
• PAL: Proxy-Guided Black-Box Attack on Large Language Models [55.57987172146731]
  Large Language Models (LLMs) have surged in popularity in recent months, but they have demonstrated capabilities to generate harmful content when manipulated. We introduce the Proxy-Guided Attack on LLMs (PAL), the first optimization-based attack on LLMs in a black-box query-only setting. Our attack achieves 84% attack success rate (ASR) on GPT-3.5-Turbo and 48% on Llama-2-7B, compared to 4% for the current state of the art.
  arXiv  Detail & Related papers  (2024-02-15T02:54:49Z)
• She had Cobalt Blue Eyes: Prompt Testing to Create Aligned and Sustainable Language Models [2.6089354079273512]
  Recent events indicate ethical concerns around conventionally trained large language models (LLMs) We introduce a test suite of prompts to foster the development of aligned LLMs that are fair, safe, and robust. Our test suite evaluates outputs from four state-of-the-art language models: GPT-3.5, GPT-4, OPT, and LLaMA-2.
  arXiv  Detail & Related papers  (2023-10-20T14:18:40Z)
• Verbosity Bias in Preference Labeling by Large Language Models [10.242500241407466]
  We examine the biases that come along with evaluating Large Language Models (LLMs) We take a closer look into verbosity bias -- a bias where LLMs sometimes prefer more verbose answers even if they have similar qualities.
  arXiv  Detail & Related papers  (2023-10-16T05:19:02Z)
• SCALE: Synergized Collaboration of Asymmetric Language Translation Engines [105.8983433641208]
  We introduce a collaborative framework that connects compact Specialized Translation Models (STMs) and general-purpose Large Language Models (LLMs) as one unified translation engine. By introducing translation from STM into the triplet in-context demonstrations, SCALE unlocks refinement and pivoting ability of LLM. Our experiments show that SCALE significantly outperforms both few-shot LLMs (GPT-4) and specialized models (NLLB) in challenging low-resource settings.
  arXiv  Detail & Related papers  (2023-09-29T08:46:38Z)
• A Trip Towards Fairness: Bias and De-Biasing in Large Language Models [1.987426401990999]
  Cheap-to-Build Very Large-Language Models (CtB-LLMs) with affordable training are emerging as the next big revolution in natural language processing and understanding. In this paper, we performed a large investigation of the bias of three families of CtB-LLMs. We show that debiasing techniques are effective and usable.
  arXiv  Detail & Related papers  (2023-05-23T09:35:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.