Analyzing and Mitigating Cultural Hallucinations of Commercial Language Models in Turkish

Опубликована: Май 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.

Язык: Английский

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Assessing Visual Hallucinations in Vision-Enabled Large Language Models

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

Опубликована: Май 9, 2024

Recent advancements in vision-enabled large language models have prompted a renewed interest evaluating their capabilities and limitations when interpreting complex visual data. The current research employs ImageNet-A, dataset specifically designed with adversarially selected images that challenge standard AI models, to test the processing robustness of three prominent models: GPT-4 Vision, Google Gemini 1.5, Anthropic Claude 3. Quantitative analyses revealed notable disparities misclassification rates types errors among these indicating variation ability handle adversarial inputs effectively. Vision demonstrated commendable robustness, whereas 1.5 excelled speed efficiency. 3, while showing intermediate accuracy levels, displayed significant propensity for contextual misinterpretations. Qualitative evaluations further assessed relevance plausibility models' hallucinations, uncovering challenges achieving human-like understanding ambiguous or scenes. findings emphasize necessity improvements semantic understanding. Future directions include enhancing refining evaluation metrics better capture qualitative aspects understanding, fostering interdisciplinary collaborations develop systems more nuanced interpretive abilities. study underscores ongoing journey towards can match human perceptual skills, highlighting both progress made considerable remain.

Язык: Английский

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Hallucination Reduction in Large Language Models with Retrieval-Augmented Generation Using Wikipedia Knowledge

Опубликована: Май 30, 2024

Natural language understanding and generation have seen great progress, yet the persistent issue of hallucination undermines reliability model outputs. Introducing retrieval-augmented (RAG) with external knowledge sources, such as Wikipedia, presents a novel significant approach to enhancing factual accuracy coherence in generated content. By dynamically integrating relevant information, Mistral demonstrates substantial improvements precision, recall, overall quality responses. This research offers robust framework for mitigating hallucinations, providing valuable insights deploying reliable AI systems critical applications. The comprehensive evaluation underscores potential RAG advance performance trustworthiness large models.

Язык: Английский

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Reducing Hallucinations in Large Language Models Through Contextual Position Encoding

Опубликована: Май 31, 2024

In natural language processing, maintaining factual accuracy and minimizing hallucinations in text generation remain significant challenges. Contextual Position Encoding (CPE) presents a novel approach by dynamically encoding positional information based on the context of each token, significantly enhancing model's ability to generate accurate coherent text. The integration CPE into Mistral Large model resulted marked improvements precision, recall, F1-score, demonstrating superior performance over traditional methods. Furthermore, enhanced architecture effectively reduced hallucination rates, increasing reliability generated outputs. Comparative analysis with baseline models such as GPT-3 BERT confirmed efficacy CPE, highlighting its potential influence future developments LLM architecture. results underscore importance advanced techniques improving applicability large across various domains requiring high accuracy.

Язык: Английский

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A Longchain Approach to Reduce Hallucinations in Large Language Models

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

Опубликована: Июнь 5, 2024

The increasing deployment of natural language processing models in critical domains necessitates addressing the issue hallucinations, where generated outputs may be factually incorrect or nonsensical. longchain approach, which involves an iterative refinement process, offers a novel and significant method to mitigate hallucinations by enhancing both accuracy coherence model outputs. methodology involved modifying GPT-3 architecture incorporate additional layers for intermediate evaluations corrections, followed rigorous training evaluation using MMLU dataset. Quantitative results demonstrated that modified significantly outperformed baseline across various performance metrics, including precision, recall, F1-score, logical coherence, hallucination rate. Qualitative analysis further supported these findings, showcasing practical benefits approach producing accurate contextually relevant study emphasizes theoretical foundations learning continuous improvement, providing robust framework reliability models. implications findings are substantial applications healthcare, legal advice, education, generation reliable text is paramount. By reducing improving contributes development more trustworthy effective

Язык: Английский

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Enhancing Contextual Understanding of Mistral LLM with External Knowledge Bases

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

Опубликована: Апрель 5, 2024

Abstract This study explores the enhancement of contextual understanding and factual accuracy in Language Learning Models (LLMs), specifically Mistral LLM, through integration external knowledge bases. We developed a novel methodology for dynamically incorporating real-time information from diverse sources, aiming to address inherent limitations LLMs rooted their training datasets. Our experiments demonstrated significant improvements accuracy, precision, recall, F1 score, alongside qualitative enhancements response relevance accuracy. The research also tackled computational challenges integrating knowledge, ensuring model's efficiency practical applicability. work not only highlights potential bases augment capabilities but sets stage future advancements creating more intelligent, adaptable, contextually aware AI systems. findings contribute broader field NLP by offering insights into overcoming traditional LLMs, presenting step toward developing systems with enhanced real-world applicability accessibility.

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

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

Опубликована: Июнь 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.

Язык: Английский

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Comparative Analysis of Finetuning Strategies and Automated Evaluation Metrics for Large Language Models in Customer Service Chatbots

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

Опубликована: Авг. 13, 2024

Customer service chatbots have become integral to the efficient operation of many businesses, offering scalable solutions handle vast volumes customer interactions. However, ensuring that these generate accurate, contextually appropriate, and coherent responses remains a significant challenge, particularly as complexity queries increases. The research presented introduces novel approach optimizing chatbot performance through an in-depth comparison various finetuning strategies evaluation metrics, demonstrating Domain-Adaptive Pretraining (DAPT) provides superior accuracy, robustness, relevance in scenarios. A comprehensive experimental analysis was conducted across three distinct large language models, revealing while DAPT excels producing high-quality, resilient responses, parameter-efficient methods offer resource-efficient alternative suitable for environments with limited computational capabilities. study’s findings critical implications development deployment chatbots, emphasizing need careful selection aligned specific operational requirements.

Язык: Английский

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Gradual Improvement of Contextual Understanding in Large Language Models via Reverse Prompt Engineering

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

Опубликована: Авг. 15, 2024

The increasing demand for more sophisticated and contextually aware language generation has highlighted the limitations of traditional models, which often struggle to maintain relevance accuracy across diverse dynamic contexts. novel concept reverse prompt engineering, introduced in this research, represents a significant breakthrough by enabling prompts that are retrospectively aligned with desired outputs, thereby enhancing model's ability adapt varying contexts precision. Through fine-tuning Mistral model, combined integration research achieved substantial improvements context-specific generation, demonstrating enhanced performance wide range tasks, including summarization, translation, question answering. results demonstrate importance modeling adaptive together contribute accurate relevant output, offering robust framework future advancements model development. methodologies developed study not only advance current understanding context adaptation models but also pave way versatile scalable applications various domains.

Язык: Английский

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