Analyzing and Mitigating Cultural Hallucinations of Commercial Language Models in Turkish

Published: May 7, 2024

In an era where artificial intelligence is increasingly interfacing with diverse cultural contexts, the ability of language models to accurately represent and adapt these contexts paramount importance.The present research undertakes a meticulous evaluation three prominent commercial models-Google Gemini 1.5, ChatGPT-4, Anthropic's Claude 3 Sonet-with focus on their handling Turkish language.Through dual approach quantitative metrics, Cultural Inaccuracy Score (CIS) Sensitivity Index (CSI), alongside qualitative analyses via detailed case studies, disparities in model performances were highlighted.Notably, Sonet exhibited superior sensitivity, underscoring effectiveness its advanced training methodologies.Further analysis revealed that all demonstrated varying degrees competence, suggesting significant room for improvement.The findings emphasize necessity enriched diversified datasets, innovative algorithmic enhancements, reduce inaccuracies enhance models' global applicability.Strategies mitigating hallucinations are discussed, focusing refinement processes continuous foster improvements AI adaptiveness.The study aims contribute ongoing technologies, ensuring they respect reflect rich tapestry human cultures.

Language: Английский

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Dynamic Supplementation of Federated Search Results for Reducing Hallucinations in LLMs

Published: June 6, 2024

The increasing use of AI-generated content has highlighted the critical issue hallucinations, where models produce factually incorrect or misleading outputs. Addressing this challenge, a novel approach dynamically supplements federated search engine results in real-time to significantly reduce hallucinations and enhance response accuracy. methodology involves integrating data from multiple engines into responses generated by Mistral Large model, thereby providing more accurate contextually appropriate output. Comprehensive evaluation using Microsoft PromptBench dataset demonstrates substantial improvements accuracy, relevance, reduction hallucinations. Quantitative performance metrics, statistical analysis, detailed case studies confirm effectiveness dynamic supplementation approach. findings suggest significant implications for developing reliable AI applications across various domains, emphasizing potential hybrid systems that combine strengths large language information retrieval. Future research directions include refining triggering mechanisms, expanding sources, optimizing process further scalability.

Language: Английский

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Exploiting Privacy Vulnerabilities in Open Source LLMs Using Maliciously Crafted Prompts

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

Published: June 18, 2024

The proliferation of AI technologies has brought to the forefront concerns regarding privacy and security user data, particularly with increasing deployment powerful language models such as Llama. A novel concept investigated involves inducing breaches through maliciously crafted prompts, highlighting potential for these inadvertently reveal sensitive information. study systematically evaluated vulnerabilities Llama model, employing an automated framework test analyze its responses a variety inputs. Findings significant flaws, demonstrating model's susceptibility adversarial attacks that could compromise privacy. Comprehensive analysis provided insights into types prompts most effective in eliciting private demonstrates necessity robust regulatory frameworks advanced measures. implications findings are profound, calling immediate action enhance protocols LLMs protect against breaches. Enhanced oversight continuous innovation privacy-preserving techniques crucial ensuring safe various applications. derived from this research contribute deeper understanding LLM urgent need improved safeguards prevent data leakage unauthorized access.

Language: Английский

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Knowledge Accuracy and Reducing Hallucinations in LLMs via Dynamic Domain Knowledge Injection

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

Published: June 7, 2024

Natural language processing has seen substantial progress with the development of highly sophisticated models capable understanding and generating human-like text. However, a persistent challenge remains in enhancing accuracy these when dealing domain-specific knowledge, particularly avoiding hallucinations or plausible but incorrect information. The dynamic domain knowledge injection mechanism introduced this research represents significant advancement by allowing continuous integration prioritisation specialised information, thereby improving model's performance reliability. By dynamically adjusting hidden weights GPT-Neo based on relevance accuracy, modified model achieved higher precision, recall, F1-scores, exhibited reduced hallucination rates across diverse domains such as cybersecurity, medical financial data, legal documents. A comprehensive evaluation framework, including benchmark creation metrics, validated effectiveness approach, demonstrating that can substantially enhance utility large fields. results highlight transformative potential method, offering robust pathway for more accurate contextually aware models. Detailed analysis ablation studies further elucidate contributions each component within modification process, providing critical insights into optimisation future applications innovative approach.

Language: Английский

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A Multimodal Approach to Estimate Large Language Model Improvisational Capabilities

Published: May 10, 2024

Evaluating the improvisational capabilities of large language models (LLMs) like ChatGPT-4, Mistral, and Anthropic Claude across textual, visual, psychological domains provides critical insights into their functionality potential applications. The research demonstrates significant variances in ability these to generate creative, contextually appropriate responses, visually coherent images from textual descriptions, emotionally nuanced interactions. ChatGPT-4 excelled improvisation, showcasing its capacity produce linguistically rich innovative content that pushes boundaries traditional text-based AI Mistral distinguished itself generation visual content, effectively translating abstract prompts detailed relevant images, indicating utility creative design fields. performed exceptionally well adaptability, interpreting responding emotional cues with a high degree empathy accuracy, suitable for customer service therapeutic findings underscore diverse LLMs, highlighting transform industries require understanding complex content. Future should focus on enhancing reliability varied scenarios, improving ethical deployment, exploring hybrid approaches leverage unique strengths.

Language: Английский

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Bring Retrieval Augmented Generation to Google Gemini via External API: An Evaluation with BIG-Bench Dataset

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

Published: May 10, 2024

The integration of Retrieval Augmented Generation (RAG) into existing large language models represents a significant shift towards more dynamic and context-aware AI systems. In this work, Google Gemini, state-of-the-art model, has been enhanced with RAG capabilities to leverage external, real-time data sources during the response generation process. This augmentation aims address traditional limitations models, particularly in generating responses that require up-to-date information adaptability complex user queries. performance RAG-enhanced Gemini was rigorously evaluated using BIG-Bench dataset, which includes tasks designed test bounds terms reasoning, contextuality, factual accuracy. Quantitative results from evaluation demonstrate marked improvements accuracy contextual relevance across various tasks, indicating effectiveness enhancing model performance. Qualitative assessments further support these findings, highlighting model’s improved ability generate precise relevant responses. However, also introduces challenges related computational efficiency scalability, emphasizing need for optimization. paper discusses potential future research directions, including application other datasets, exploration different configurations, development sophisticated handling techniques enhance applicability. ongoing advancement technologies promises significantly broaden utility AI-driven systems real-world applications, making them adaptable useful diverse scenarios.

Language: Английский

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Augmenting Large Language Models with Reverse Proxy Style Retrieval Augmented Generation for Higher Factual Accuracy

Published: May 31, 2024

The increasing reliance on artificial intelligence for generating human-like text has brought attention to the critical issue of factual accuracy in language models. Introducing a novel approach, this research augments Llama model with reverse proxy-style Retrieval Augmented Generation (RAG) mechanism, significantly enhancing and coherence generated text. By dynamically incorporating relevant up-to-date information from diverse external data sources, RAG-augmented addresses inherent limitations static training data, thereby more reliable contextually appropriate responses. experimental evaluation, using precision, recall, F1-score, BLEU, ROUGE metrics, demonstrated substantial improvements, affirming effectiveness proposed system. findings reveal potential integrating retrieval mechanisms generative models achieve higher quality generation, offering valuable insights future practical applications fields where precision reliability are paramount.

Language: Английский

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Prompting and In-Context Learning: Optimizing Prompts for Mistral Large

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

Published: May 17, 2024

The exploration of the synergy between prompting and in-context learning reveals significant improvements in performance language models when tailored instructions relevant context are integrated. research delves into various prompt designs, assessing their impact on tasks such as text summarisation, machine translation, question-answering. Prompts that include clear, explicit contextual information significantly enhance model outputs terms accuracy, coherence, relevance. Experiments with Mistral Large demonstrate adaptive prompting, which dynamically adjusts based real-time interactions, can further refine performance. Challenges balancing amount to avoid overload sensitivity responses subtle changes phrasing addressed. study's findings underscore critical role effective engineering integration maximising potential models. Future directions developing systematic methods for design, optimising information, exploring cross-task generalisation. This contributes valuable insights guiding informing models, paving way more intelligent AI systems across diverse applications.

Language: Английский

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Large Language Model Understands Chinese Better with Mega Tokenization

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

Published: June 10, 2024

The rapid evolution of natural language processing has seen significant advancements in models, particularly for languages with simpler orthographies. However, challenges persist accurately and understanding complex morphological structures, such as Chinese, due to the limitations traditional tokenization methods. Introducing mega tokenization, which involves significantly larger tokens, represents a novel transformative approach that enhances semantic preservation contextual coherence sophisticated character sequences. study compares performance an adapted model against standard model, demonstrating substantial improvements across tasks machine translation, text summarisation, question answering. Through rigorous evaluation statistical analysis, shows superior metrics, indicating effectiveness addressing unique posed by Chinese language. implications this extend various applications, underscoring its potential revolutionise multilingual high-stakes environments. Future research directions are proposed further optimise expand applicability diverse linguistic contexts.

Language: Английский

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Evaluating Abstract Reasoning and Problem-Solving Abilities of Large Language Models Using Raven's Progressive Matrices

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

Published: June 11, 2024

Artificial intelligence has rapidly evolved, leading to the development of powerful models capable performing complex cognitive tasks. Evaluating abilities these through established human tests such as Raven's Progressive Matrices (RPM) offers a novel and significant approach understanding their abstract reasoning capabilities. The study adapted RPM for text-based interactions, enabling evaluation Mistral Llama without intervention. Results revealed that both surpass average performance in overall accuracy, demonstrating advanced problem-solving skills. However, analysis also highlighted variability across different types tasks, with excelling sequential pattern recognition showing weaknesses spatial awareness. These findings provide valuable insights into strengths limitations Llama, offering comprehensive guiding future advancements artificial intelligence.

Language: Английский

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