Evaluating Privacy Compliance in Commercial Large Language Models - ChatGPT, Claude, and Gemini

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: July 26, 2024

The integration of artificial intelligence systems into various domains has raised significant privacy concerns, necessitating stringent regulatory measures to protect user data. Evaluating the compliance commercial large language models (LLMs) such as ChatGPT-4o, Claude Sonet, and Gemini Flash under EU AI Act presents a novel approach, providing critical insights their adherence standards. study utilized hypothetical case studies assess practices these LLMs, focusing on data collection, storage, sharing mechanisms. Findings revealed that ChatGPT-4o exhibited issues with minimization access control, while Sonet demonstrated robust effective security measures. However, showed inconsistencies in collection higher incidence anonymization failures. comparative analysis underscored importance tailored strategies continuous monitoring ensure compliance. These results provide valuable for developers policymakers, emphasizing necessity multifaceted approach deployment LLMs.

Language: Английский

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Mitigating Structural Hallucination in Large Language Models with Local Diffusion

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: July 4, 2024

Large language models (LLMs) often produce text with inaccuracies, logical inconsistencies, or fabricated information, known as structural hallucinations, which undermine their reliability and trustworthiness. Implementing local diffusion mechanisms within the Mistral LLM architecture has demonstrated significant potential in addressing these issues, enhancing both accuracy coherence of generated text. The modified model exhibited substantial improvements across various performance metrics, including accuracy, precision, recall, F1 score, validated through rigorous statistical testing. architectural adjustments, involving integration layers, facilitated better information propagation reduced occurrence structurally flawed outputs. Quantitative analyses highlighted model's enhanced performance, while qualitative comparisons revealed its improved integrity factual accuracy. Additionally, error analysis a notable reduction frequency errors, further affirming effectiveness approach. findings reveal transformative mitigating hallucinations advancing field natural processing.

Language: Английский

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Implementing An Automated Socratic Method to Reduce Hallucinations in Large Language Models

Published: July 27, 2024

The increasing reliance on AI-driven applications necessitates robust methods to ensure the accuracy and reliability of information generated by these systems. integration Socratic method within AI models represents a novel approach addressing critical issue hallucinations, where produce factually incorrect or logically inconsistent outputs. This research presents an innovative methodology that leverages structured questioning, self-critique mechanisms, iterative training processes, automated evaluation metrics systematically enhance quality responses Llama model. results demonstrate significant improvements in coherence, factual accuracy, relevance, logical consistency, thereby reducing incidence hallucinations. study's findings have important implications for deployment high-stakes applications, suggesting can be effectively scaled adapted across various domains develop more reliable trustworthy Future work may explore further refinements questioning algorithms expand achieve even greater enhancements model performance, paving way advancements safety robustness.

Language: Английский

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Enhancing Explainability in Large Language Models Through Belief Change: A Simulation-Based Approach

Authorea (Authorea), Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 20, 2024

Artificial intelligence systems, particularly those deployed in high-stakes environments, require a high degree of transparency and explainability to ensure that their decisions can be understood trusted. Traditional approaches enhancing often rely on post-hoc methods fail fully capture the internal reasoning processes complex models. In this research, novel integration Belief Change Theory was employed address challenge, offering systematic framework for belief revision directly influences decisionmaking process model. The proposed methodology implemented Llama model, which modified incorporate mechanisms capable handling contradictory information generating coherent explanations. Through series simulations, model demonstrated significant improvements consistency, accuracy, overall explainability, outperforming traditional models lack integrated management systems. findings highlight potential not only enhance AI systems but also provide foundation more dynamic interactive forms interpretability. research opens new avenues development are both powerful accountable, paving way adoption critical decision-making contexts.

Language: Английский

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Assessing the Response Strategies of Large Language Models Under Uncertainty: A Comparative Study Using Prompt Engineering

Published: Aug. 1, 2024

The ability of artificial intelligence to understand and generate human language has transformed various applications, enhancing interactions decision-making processes. Evaluating the fallback behaviors models under uncertainty introduces a novel approach understanding improving their performance in ambiguous or conflicting scenarios. research focused on systematically analyzing ChatGPT Claude through series carefully designed prompts introduce different types uncertainty, including questions, vague instructions, information, insufficient context. Automated scripts were employed ensure consistency data collection, responses evaluated using metrics such as accuracy, consistency, mechanisms, response length, complexity. results highlighted significant differences how handle with demonstrating superior accuracy stability, more frequent use proactive strategies manage inputs. study's findings provide valuable insights for ongoing development refinement models, emphasizing importance integrating advanced mechanisms adaptive enhance robustness reliability.

Language: Английский

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Enhanced Cross-Domain Named Entity Recognition of Large Language Model through Label Alignment

Published: Aug. 1, 2024

Named Entity Recognition (NER) is a crucial component in extracting structured information from unstructured text across various domains. A novel approach has been developed to address the variability domain-specific annotations through integration of unified label schema, significantly enhancing cross-domain NER performance. The study involved comprehensive modifications Mistral Large model, including adjustments its architecture, output layer, and loss function, incorporate aligned schema effectively. methodology encompassed rigorous data collection, preprocessing, evaluation processes, ensuring robust model training validation. Evaluation metrics such as precision, recall, F1-score, accuracy demonstrated substantial improvements, validating efficacy alignment algorithm. research highlights model's ability generalize entity recognition capabilities diverse domains, making it adaptable linguistic contextual details. implications extend numerous applications reliant on accurate recognition, retrieval, question answering, knowledge base population, demonstrating broader impact findings. Through these significant advancements, contributes development more intelligent adaptive systems capable handling complexities evolving textual environments.

Language: Английский

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Quantifying Chaotic Semantic States in Large Language Models Using Automated Prompt Analysis

Published: Aug. 2, 2024

In recent years, artificial intelligence has made impressive strides in generating coherent and contextually appropriate text, demonstrating significant potential across various domains.The novel concept of measuring the internal chaotic semantic state large language models through carefully crafted prompts offers a unique perspective on understanding enhancing robustness reliability these models.The methodology employed involved diverse prompts, analyzing model's responses using statistical computational techniques, calculating metrics such as entropy, coherence scores, response variability.The findings highlighted variability unpredictability states, particularly creative ambiguous contexts, emphasizing need for continuous advancements model architecture training strategies.Comparative analysis different versions ChatGPT revealed differences stability, underscoring importance refining designs to achieve balance between flexibility stability.The study's contributions provide valuable insights into development more robust reliable models, paving way future research innovation field.

Language: Английский

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Optimizing Large Language Models with Multi-Degree Low-Rank Approximations

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 27, 2024

The increasing computational demands and resource requirements of advanced neural network models have created a growing need for efficient methods to enhance their scalability deployment, particularly in environments with limited hardware capabilities. Addressing this challenge, the novel application multi-degree low-rank approximations provides significant breakthrough, enabling substantial reductions memory usage costs while preserving high levels performance. Experiments conducted on Mistral model demonstrated that approach can effectively balance trade-offs between complexity accuracy, achieving reduced perplexity improved classification performance across range tasks. use varying degrees rank reduction allowed tailored optimization, enhancing model's adaptability different task operational environments. findings suggest are not only viable solution optimizing large-scale networks but also versatile tool extending applicability sophisticated language resource-constrained settings. This opens up new possibilities deployment processing capabilities real-time applications, mobile devices, other platforms where efficiency is critical.

Language: Английский

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Dynamic Contextual Alignment Mechanisms for Improving the Internal Representational Consistency in Large Language Models

Published: Nov. 18, 2024

The increasing complexity of language models naturally demands innovative approaches to maintain internal representational consistency. This paper introduces Dynamic Contextual Alignment Mechanisms, a novel framework designed enhance semantic coherence within large models. By integrating adaptive recalibration strategies, the proposed mechanism aligns intermediate representations across multiple layers, thereby reducing contextual ambiguities and improving interpretative processes Comprehensive evaluations demonstrate significant reductions in perplexity attention entropy, alongside improvements scores, indicating mechanism's efficacy refining understanding. Comparative analyses reveal that, unlike traditional methods relying on fine-tuning or auxiliary this approach inherently enhances alignment without substantial computational overhead. findings potential Mechanisms advance robustness adaptability diverse applications, addressing fundamental challenges setting foundation for future developments field.

Language: Английский

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