Dynamic Neural Embedding for Contextual Regeneration in Large Language Models DOI Open Access

George Kuse,

Arthur E. Rosenbaum,

Isabella Chanterelle

et al.

Published: Nov. 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.

Language: Английский

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

Oliver Cartwright,

H. Flanders Dunbar,

Theo Radcliffe

et al.

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

Published: July 26, 2024

Abstract 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

Citations

13
A Longchain Approach to Reduce Hallucinations in Large Language Models DOI Creative Commons

Jinchao Li,

Quan Hong

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

Published: June 5, 2024

Abstract 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

Language: Английский

Citations

12
Investigating Hallucination Tendencies of Large Language Models in Japanese and English DOI Creative Commons

Hiromi Tsuruta,

Rio Sakaguchi

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

Published: June 4, 2024

Abstract The increasing reliance on artificial intelligence for natural language processing has brought to light the issue of hallucinations in models, where models generate content that appears plausible but is factually incorrect. Exploring comparative hallucination tendencies Japanese and English reveals significant differences, highlighting importance understanding language-specific challenges model performance. A rigorous methodology was employed quantify frequency severity hallucinations, with comprehensive data collection from diverse sources both languages. Quantitative analysis indicated a higher propensity responses, attributed complex syntactical contextual structures language. Qualitative examples provided concrete illustrations errors encountered, demonstrating impact linguistic cultural factors. findings emphasize necessity more linguistically contextually rich training datasets, along advanced fact-checking mechanisms, improve reliability models. study's implications extend development tailored strategies enhancing accuracy across different languages, contributing broader goal creating robust trustworthy systems global applications.

Language: Английский

Citations

9
A Comparative Study of Cultural Hallucination in Large Language Models on Culturally Specific Ethical Questions DOI Creative Commons

Jiajing Zhao,

Cheng Huang,

X. nuan. Li

et al.

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

Published: June 12, 2024

Abstract Rapid advancements in natural language processing have led to the development of highly sophisticated models capable generating human-like text, yet challenges remain ensuring that these produce culturally accurate and ethically consistent responses. The novel concept this study lies comprehensive evaluation ChatGPT 4o Gemini 1.5 Flash on specific ethical questions, providing a detailed comparison their performance across diverse cultural contexts. Automated metrics, including semantic similarity, relevance, consistency, were employed assess models' capabilities, revealing significant insights into strengths limitations. results indicated while both exhibit high relevance notable differences various regions suggest areas for further improvement. Statistical analysis confirmed significance differences, emphasizing necessity ongoing refinement training methodologies. demonstrates importance integrating deeper frameworks model development, contributing valuable knowledge field AI ethics competence.

Language: Английский

Citations

4
Evaluating Abstract Reasoning and Problem-Solving Abilities of Large Language Models Using Raven's Progressive Matrices DOI Creative Commons

C. C. Zhang,

Liuyun Wang

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

Published: June 11, 2024

Abstract 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

Citations

3
Gradual Improvement of Contextual Understanding in Large Language Models via Reverse Prompt Engineering DOI

Sebastian Femepid,

Lachlan Hatherleigh,

William Kensington

et al.

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

Published: Aug. 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.

Language: Английский

Citations

3
Automated Comparative Analysis of Visual and Textual Representations of Logographic Writing Systems in Large Language Models DOI Creative Commons

Peng Shao,

Ruichen Li,

Kai Qian

et al.

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

Published: Aug. 16, 2024

Abstract 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.

Language: Английский

Citations

3
Evaluating Large Language Models through the Lens of Linguistic Proficiency and World Knowledge: A Comparative Study DOI

Nathan Atox,

Mason Clark

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

Published: Aug. 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.

Language: Английский

Citations

3
Game-Theoretic Approaches for Step-wise Controllable Text Generation in Large Language Models DOI

Daniel Sefeni,

Michael Johnson,

Joshua Lee

et al.

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

Published: Sept. 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.

Language: Английский

Citations

3
Mitigating Hallucinations in LLM Using K-means Clustering of Synonym Semantic Relevance DOI Creative Commons

Lin He,

Keqin Li

Published: June 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

Language: Английский

Citations

2