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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Mitigating Hallucinations in LLM Using K-means Clustering of Synonym Semantic Relevance

Опубликована: Июнь 12, 2024

Language models are prone to generating hallucinations, which significantly undermine their reliability and usefulness in critical applications. Introducing a novel approach that combines semantic relevance scoring with K-means clustering, our methodology enhances the model’s accuracy reduces occurrence of hallucinations. By integrating these techniques, model can prioritize contextually appropriate synonyms, resulting more coherent factually correct outputs. The experimental results demonstrate substantial improvements accuracy, relevance, marked reduction hallucinations across various tasks. Comprehensive evaluation using diverse metrics demonstrates robustness effectiveness modifications, highlighting potential for practical deployment applications where paramount. This study affirms viability combining clustering techniques enhance performance language models, contributing development reliable effective wide range

Язык: Английский

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Assessing the Ineffectiveness of Synthetic Reinforcement Learning Feedback in Fine-Tuning Large Language Models

Опубликована: Авг. 6, 2024

The rapid evolution of artificial intelligence has brought significant advancements in various applications, yet fine-tuning models to align outputs with user needs and ethical standards remains a challenging endeavor. Introducing synthetic reinforcement learning feedback provides novel scalable approach this challenge, bypassing the logistical financial burdens human evaluators. Through comprehensive experimentation open-source Llama model, improvements were observed performance metrics such as coherence, relevance, informativeness, factual accuracy, demonstrating efficacy mechanisms. study's methodology involved leveraging automated reward metrics, iterative parameter updates, sophisticated optimization techniques, culminating robust framework for model fine-tuning. Statistical validation demonstrated reliability improvements, while detailed analysis highlighted both potential limitations systems. findings offer substantial contributions field, providing replicable blueprint future research practical insights into optimization. implications large-scale deployments AI systems are profound, suggesting that mechanisms can significantly enhance adaptability language applications.

Язык: Английский

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A Longchain Approach to Reduce Hallucinations in Large Language Models

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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Cross-Lingual Factual Accuracy and Ideological Divergence in Large Language Models

Опубликована: Июнь 10, 2024

The novel concept of cross-lingual content factual accuracy verification explores the consistency and reliability responses produced by such models when posed with identical questions in English Chinese. This study meticulously analyzed performance ChatGPT Google Gemini, revealing high alignment but notable divergences ideologically sensitive areas, attributed to cultural ideological biases training data. A comprehensive methodology incorporating both quantitative metrics qualitative assessments was employed evaluate capabilities these models. results demonstrate potential language multilingual applications while highlighting critical need for bias mitigation strategies. implications extend enhancing development deployment AI systems diverse contexts, emphasizing importance neutrality handling information. research contributes significantly understanding strengths limitations verification, providing a foundation future improvements methodologies applications.

Язык: Английский

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

Опубликована: Авг. 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.

Язык: Английский

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

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

Опубликована: Авг. 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.

Язык: Английский

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Dynamic Neural Embedding for Contextual Regeneration in Large Language Models

Опубликована: Ноя. 25, 2024

A novel embedding methodology capable of dynamic realignment with evolving contextual inputs is introduced, addressing longstanding challenges in maintaining coherence across extended sequences. The proposed approach integrates a real-time regeneration mechanism, enhancing the ability language models to retain semantic consistency through adaptive adjustments. By incorporating feedback-driven token realignment, framework ensures logical continuity generative tasks without incurring significant computational overhead. Quantitative analyses demonstrate gains context retention and fidelity multiple benchmark datasets, marked reduction error propagation during sequential interactions. system’s scalability evident its efficient handling input lengths, robust performance such as summarization, machine translation, domain-specific text processing. Through integration kernel-based approximations hierarchical attention mechanisms, optimizes resource usage while sustaining high accuracy complex linguistic representations. Comparative studies highlight model's adaptability specialized vocabularies, particularly fields requiring understanding. robustness design further validated low-resource ambiguous scenarios, where conventional methods exhibit degradation. Error analysis demonstrates effectiveness mechanism reducing cumulative inaccuracies over iterative Results confirm framework’s capacity balance depth, setting precedent for future advancements embedding-based architectures. redefines boundaries model capabilities, achieving an unprecedented synthesis efficiency, adaptability, coherence. findings offer substantial contributions evolution processing architectures, establishing innovation.

Язык: Английский

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