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

Exploiting Privacy Vulnerabilities in Open Source LLMs Using Maliciously Crafted Prompts DOI Creative Commons

Géraud Choquet,

Aimée Aizier,

Gwenaëlle Bernollin

et al.

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

Published: June 18, 2024

Abstract 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

Citations

11
Boosting Long-term Factuality in Large Language Model with Real-World Entity Queries DOI Creative Commons

L Davies,

Samantha Bellington

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

Published: Aug. 2, 2024

Abstract 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

Language: Английский

Citations

6
Mitigating Hallucinations in Large Language Models with Sliding Generation and Self-Checks DOI Creative Commons

F. EUGENE HARRINGTON,

Elliot Rosenthal,

Miles Swinburne

et al.

Published: Aug. 6, 2024

LLMs have demonstrated strong capabilities in generating human-like text and understanding complex linguistic patterns; however, they are prone to plausiblesounding information that is factually incorrect, known as hallucinations, which poses a significant challenge for applications requiring high accuracy reliability. The proposed methodologies, Sliding Generation Self-Checks, introduce novel techniques mitigate hallucinations through structured segmentation, iterative refinement, multi-step verification processes, enhancing the factual consistency of LLM outputs. technique improves contextual relevance by dividing input prompts into overlapping segments aggregating responses, while Self-Checks mechanism ensures internal rephrasing posing related questions, thereby reducing erroneous Comprehensive evaluations efficacy these integrated approaches, highlighting marked improvements reliability across various domains, emphasizing their potential deployment high-stakes environments where integrity crucial. This research contributes advancement AI technology, providing robust framework developing more trustworthy effective capable handling sensitive tasks.

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
Enhancing Inference Efficiency and Accuracy in Large Language Models through Next-Phrase Prediction DOI Creative Commons

Cegu Vima,

H. Bosch,

John Harringstone

et al.

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

Published: Aug. 7, 2024

Abstract The ability to generate coherent and contextually relevant text is increasingly important in a variety of applications, prompting the need for more sophisticated language models. Our novel approach next-phrase prediction within Llama 2 model architecture significantly enhances both accuracy efficiency generation, setting it apart from traditional next-word methods. Through implementation dual-stage encoder-decoder framework, integrated attention mechanisms, reinforcement learning techniques, modified achieves substantial improvements BLEU ROUGE scores, as well reductions perplexity, latency, computational resource usage. Extensive evaluations across diverse datasets demonstrate model's robustness generalizability, showing its potential advance applications reliant on advanced modeling capabilities. research highlights importance continual innovation optimizing architectures training methodologies meet growing demands various natural processing tasks. By systematically addressing limitations existing approaches, study contributes valuable insights field, paving way efficient accurate models real-time applications.

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
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
Implementing An Automated Socratic Method to Reduce Hallucinations in Large Language Models DOI Open Access

Hugo Underwood,

Zoe Fenwick

Published: July 27, 2024

The increasing reliance on AI-driven applications necessitates robust methods to ensure the accuracy and reliability of information generated by these systems. integration Socratic method within AI models represents a novel approach addressing critical issue hallucinations, where produce factually incorrect or logically inconsistent outputs. This research presents an innovative methodology that leverages structured questioning, self-critique mechanisms, iterative training processes, automated evaluation metrics systematically enhance quality responses Llama model. results demonstrate significant improvements in coherence, factual accuracy, relevance, logical consistency, thereby reducing incidence hallucinations. study's findings have important implications for deployment high-stakes applications, suggesting can be effectively scaled adapted across various domains develop more reliable trustworthy Future work may explore further refinements questioning algorithms expand achieve even greater enhancements model performance, paving way advancements safety robustness.

Language: Английский

Citations

2
Efficient Conceptual Knowledge Removal in Large Language Models: Methods and Evaluations DOI Creative Commons

Miyim Dimitriou,

Daniel Rogowski,

Michael C. Anderson

et al.

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

Published: Oct. 8, 2024

Abstract The increasing use of deep neural networks has led to models that accumulate vast amounts knowledge from their training data, often retaining outdated or biased information needs be selectively removed. Novel techniques are required efficiently erase specific conceptual these while maintaining overall performance and avoiding computationally expensive re-training processes. This paper introduces a scalable framework for removal through targeted weight modification sparse fine-tuning, demonstrating how representations can isolated erased without significant degradation the model's broader capabilities. methodology achieves high precision in suppression by leveraging probing gradient-based optimization, ensuring minimal disruption general task performance. Extensive experimental evaluations confirm effectiveness proposed approach, highlighting its application scenarios where adaptive model refinement is essential both accuracy ethical integrity. Contributions field include development flexible efficient mechanism erasure, applicable across various architectures, minimizes computational overhead enhancing responsiveness dynamic requirements.

Language: Английский

Citations

2
Assessing the Response Strategies of Large Language Models Under Uncertainty: A Comparative Study Using Prompt Engineering DOI Open Access

Nehoda Lainwright,

M. Pemberton

Published: Aug. 1, 2024

The ability of artificial intelligence to understand and generate human language has transformed various applications, enhancing interactions decision-making processes. Evaluating the fallback behaviors models under uncertainty introduces a novel approach understanding improving their performance in ambiguous or conflicting scenarios. research focused on systematically analyzing ChatGPT Claude through series carefully designed prompts introduce different types uncertainty, including questions, vague instructions, information, insufficient context. Automated scripts were employed ensure consistency data collection, responses evaluated using metrics such as accuracy, consistency, mechanisms, response length, complexity. results highlighted significant differences how handle with demonstrating superior accuracy stability, more frequent use proactive strategies manage inputs. study's findings provide valuable insights for ongoing development refinement models, emphasizing importance integrating advanced mechanisms adaptive enhance robustness reliability.

Language: Английский

Citations

1