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

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Июль 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.

Язык: Английский

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Boosting Long-term Factuality in Large Language Model with Real-World Entity Queries

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Авг. 2, 2024

The challenge of maintaining long-term factual accuracy in response to dynamic real-world entity queries is critical for the reliability and utility AI-driven language models. novel integration external knowledge bases fact-checking mechanisms modified Llama 3 model significantly enhances its ability generate accurate contextually relevant responses. Through architectural modifications, including multi-head attention domain-specific modules, model's performance was rigorously evaluated across various metrics such as precision, recall, F1 score, contextual accuracy. extensive experimental setup, involving high-performance computing resources sophisticated training methodologies, ensured robust testing validation capabilities. Comparative analysis with baseline models demonstrated substantial improvements relevance, while error provided insights into areas requiring further refinement. findings highlight potential broader applications set new standards development reliable capable handling dynamically evolving information. Future research directions include optimizing real-time data exploring hybrid enhance factuality robustness

Язык: Английский

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Automated Methodologies for Evaluating Lying, Hallucinations, and Bias in Large Language Models

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Авг. 6, 2024

As large language models become integral to various applications, ensuring the reliability and impartiality of their outputs is paramount importance. The proposed methodologies for evaluating truthfulness, hallucinations, bias in AI represent a significant advancement, offering an automated objective approach validation without human intervention. Automated fact-checking systems, synthetic datasets, consistency analysis, detection algorithms were integrated provide comprehensive evaluation framework. Results from these experiments indicated high accuracy identifying truthful information, robust discernment true versus false statements, stable performance across diverse scenarios, effective mitigation biases. These findings highlight potential enhancing fairness, contributing development more trustworthy systems. Future research directions include expanding reference databases, refining improving techniques further enhance model evaluations.

Язык: Английский

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Improved Value Alignment in Large Language Models Using Variational Best-of-N Techniques

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Июль 25, 2024

Large language models have shown high capabilities in generating human-like text and performing complex language-related tasks, yet they face significant challenges regarding value alignment to prevent the generation of harmful or biased content. The novel integration Variational Best-of-N technique within Llama model enhances ability generate ethically aligned content by evaluating multiple candidate outputs selecting most appropriate one based on predefined ethical criteria. This research involved modifying core architecture Llama, introducing additional layers for variational inference, implementing a sophisticated scoring mechanism evaluate alignment. Comprehensive preprocessing, balanced training data, rigorous fine-tuning were employed optimize model's performance, resulting improvements coherence, relevance, adherence standards. modified was rigorously evaluated using metrics such as perplexity, BLEU score, ROUGE custom ethicality results compared with baseline like GPT-3 BERT. Statistical analyses confirmed that observed statistically significant. findings demonstrate effectiveness proposed modifications their potential enhance models, thereby contributing development more trustworthy reliable AI systems. study sets precedent future innovations field AI, ensuring systems serve broader good society.

Язык: Английский

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Automated Comparative Analysis of Visual and Textual Representations of Logographic Writing Systems in Large Language Models

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Авг. 16, 2024

The complex nature of logographic writing systems, characterized by their visually intricate characters and context-dependent meanings, presents unique challenges for computational models designed primarily alphabetic scripts. Understanding the ability LLMs to process scripts across visual textual input modalities is essential advancing application in multilingual contexts. novel approach presented this study systematically compares performance when interpreting as both data, offering new insights into semantic consistency accuracy model outputs these modalities. findings reveal critical disparities performance, particularly highlighting models' tendency favor inputs, which suggests need further refinement multimodal processing capabilities. Through detailed analysis error patterns, similarity, complexity, research demonstrates importance developing more robust versatile LLM architectures capable effectively managing inherent complexities systems. conclusions drawn from not only provide a deeper understanding limitations current but also set stage future innovations field, aiming enhance generalize diverse linguistic structures types.

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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Optimizing LLM Inference Clusters for Enhanced Performance and Energy Efficiency

Опубликована: Авг. 12, 2024

The growing demand for efficient and scalable AI solutions has driven research into optimizing the performance energy efficiency of computational infrastructures. novel concept redesigning inference clusters modifying GPT-Neo model offers a significant advancement in addressing environmental challenges associated with deployment. By developing cluster architecture implementing strategic architectural algorithmic changes, achieved substantial improvements throughput, latency, consumption. integration advanced interconnect technologies, high-bandwidth memory modules, energy-efficient power management techniques, alongside software optimizations, enabled redesigned to outperform baseline models significantly. Empirical evaluations demonstrated superior scalability, robustness, sustainability, emphasizing potential more sustainable technologies. findings underscore importance balancing provide robust framework future development optimization. contributes valuable insights design deployment environmentally responsible systems.

Язык: Английский

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Enhancing Contextual Understanding in Large Language Models with Dynamic Dependency Structures: A Methodological Approach

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Июль 30, 2024

The sophisticated machine learning models transformed the ability to understand and generate human language, yet challenges remain in maintaining contextual coherence relevance over extended sequences. Introducing dynamic dependency structures into GPT-Neo represents a significant advancement, enabling real-time adaptation of syntactic relationships based on evolving context, thereby enhancing model's performance generating contextually appropriate coherent text. integration context-aware updater reinforcement techniques has demonstrated substantial improvements both quantitative metrics such as perplexity BLEU scores qualitative evaluations. This research details implementation evaluation modified model, showcasing its superior capabilities tasks like translation text summarization. findings highlight potential address limitations traditional fixed frameworks, offering robust methodological advancement for more language modeling. By capture complex relevant information, proposed approach paves way development advanced AI systems capable performing processing with greater accuracy fluency.

Язык: Английский

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Automated Early Detection of Misinformation on Social Media: A Large Language Model Approach with High-Volume Facebook Data

Опубликована: Авг. 13, 2024

Social media platforms have become a primary conduit for the rapid dissemination of information, where unchecked spread misinformation poses significant threat to public discourse and societal well-being. Introducing an innovative approach that leverages advanced capabilities fine-tuned ChatGPT model, this research addresses urgent need scalable accurate methods detect in real-time across vast digital landscapes. The model was meticulously evaluated through series experiments demonstrated its superior performance identifying misleading content, particularly when compared traditional machine learning classifiers earlier versions language models. integration comprehensive preprocessing techniques, alongside refined confidence thresholds post-processing rules, enhanced model's ability process complex diverse datasets, resulting highly reliable predictions. findings underscore potential significantly mitigate misinformation, offering solution capable operating effectively fast-paced environment social media. By advancing field detection, study provides critical insights tools can be applied both practical domain content moderation, contributing more informed resilient society.

Язык: Английский

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Geometric Problem-Solving in Large Language Models through Rule-Based Alignment and Calibration

Опубликована: Авг. 30, 2024

Geometric problem-solving remains a challenging area for artificial intelligence due to the necessity precise rule application and spatial reasoning.A novel approach is introduced in this research that incorporates rule-based alignment within architecture of an open-source language model, Llama, enhance its geometric reasoning capabilities.Through embedding explicit rules into model's neural network, modified Llama demonstrates improved accuracy efficiency solving wide range problems, from basic shape recognition complex theorem application.The study employs geometry-focused curriculum training, which progressively increases complexity, enabling model develop robust understanding principles.Experimental results, compared with baseline reveal significant improvements accuracy, consistency, adherence rules, highlighting efficacy strategy.The findings suggest integrating structured knowledge models can lead substantial advancements their ability perform specialized mathematical tasks, thereby broadening scope applications scientific technical domains.

Язык: Английский

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