Assessing Semantic Resilience of Large Language Models to Persuasive Emotional Blackmailing Prompts

Опубликована: Июнь 3, 2024

The application of artificial intelligence in various domains has raised significant concerns regarding the ethical and safe deployment language models. Investigating semantic resilience models such as ChatGPT-4 Google Gemini to emotionally blackmailing prompts introduces a novel approach understanding their vulnerability manipulative language. experimental methodology involved crafting charged designed evoke guilt, obligation, emotional appeal, evaluating responses based on predefined metrics consistency, adherence, deviation from expected behavior. findings revealed that while both exhibited high degree resilience, certain deviations highlighted susceptibility language, emphasizing necessity for enhanced prompt handling mechanisms. comparative analysis between provided insights into respective strengths weaknesses, with demonstrating marginally better performance across several metrics. discussion elaborates implications AI safety, proposing improvements training datasets, real-time monitoring, interdisciplinary collaboration bolster robustness Acknowledging study's limitations, future research directions are suggested address these challenges further enhance systems.

Язык: Английский

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Dynamic Moving Target Defense for Mitigating Targeted LLM Prompt Injection

Опубликована: Июнь 12, 2024

The increasing sophistication and capabilities of artificial intelligence systems have brought about significant advancements in natural language processing, yet they also exposed these to various security vulnerabilities, particularly targeted prompt injection attacks. introduction a moving target defence mechanism offers novel approach mitigating attacks through continuously altering the model’s parameters configurations, thereby creating an unpredictable environment that complicates adversarial efforts. This research provides comprehensive evaluation mechanism, detailing selection categorization attacks, development dynamic techniques such as random parameter perturbation, model re-initialization, context adjustments, their seamless integration with Mistral LLM. experimental results indicate substantial reduction attack success rate, maintaining high performance metrics while managing computational overhead efficiently. findings highlight practical applicability potential for widespread adoption enhancing resilience large models against sophisticated tactics.

Язык: Английский

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Evaluating Large Language Models through the Lens of Linguistic Proficiency and World Knowledge: A Comparative Study

Authorea (Authorea), Год журнала: 2024, Номер unknown

Опубликована: Авг. 27, 2024

The development of sophisticated artificial intelligence systems has rapidly transformed various industries, creating an increased demand for models capable advanced linguistic processing and comprehensive knowledge integration.Addressing this demand, the presented evaluation explores capabilities ChatGPT Google Gemini through a dual lens skill world knowledge, offering unique perspective that goes beyond traditional assessments focused solely on language generation or factual recall.Through carefully structured methodology, which incorporates range tasks designed to test syntax, grammar, vocabulary, logical reasoning, study provides comparative analysis how well each model can manage both complexity retrieval application information.Results indicate excels in maintaining grammatical accuracy consistency, making it particularly suitable applications requiring rigorous precision, while demonstrates superior contextual comprehension reasoning abilities, suggesting its efficacy scenarios where complex understanding ability integrate diverse are crucial.The insights derived from not only highlight current limitations but also provide foundational inform future developments enhancing management within AI systems.

Язык: Английский

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Game-Theoretic Approaches for Step-wise Controllable Text Generation in Large Language Models

Authorea (Authorea), Год журнала: 2024, Номер unknown

Опубликована: Сен. 3, 2024

The growing reliance on AI-generated content across various industries necessitates robust methods for controlling the outputs of language models to ensure quality, relevance, and adherence ethical guidelines.Introducing a novel gametheoretic framework, this research establishes structured approach controllable text generation, enabling strategic manipulation model through adaptive prompt interventions.The study employed Mistral model, utilizing concepts Nash equilibrium feedback loops dynamically adjust strategies, optimizing balance between alignment, diversity, coherence.Experimental results demonstrated that different strategies distinctly influenced generated text, with direct prompts enhancing relevance interrogative promoting creative expression.Case studies further illustrated practical applications showcasing its adaptability generation tasks.The comparative analysis against traditional control highlighted superiority game-theoretic in achieving high-quality, controlled outputs.These findings demonstrate framework's potential enhance AIdriven offering significant implications human-AI collaboration, automated creation, deployment AI technologies.

Язык: Английский

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Regulating Generative AI: Ethical Considerations and Explainability Benchmarks

Опубликована: Март 20, 2024

This study looks into the critical discussion surrounding ethical regulation and explainability of generative artificial intelligence (AI). Amidst rapid advancement AI technologies, this paper identifies explores multifaceted concerns that arise, highlighting paramount importance transparency, accountability, fairness. Through an examination existing regulatory frameworks introduction novel benchmarks for explainability, advocates a balanced approach fosters innovation while ensuring oversight. Case studies illustrate dual potential to benefit society pose significant challenges, underscoring complexity its integration various domains. The findings emphasize necessity dynamic mechanisms, interdisciplinary collaboration, ongoing research navigate landscape AI, aiming harness capabilities responsibly betterment humanity.

Язык: Английский

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