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

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

Jason Kirchenbauer,

Caleb Barns

Published: May 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.

Language: Английский

Citations

23
Reducing Hallucinations in Large Language Models Through Contextual Position Encoding DOI Open Access

Sarah Desrochers,

James Wilson,

Matthew Beauchesne

et al.

Published: May 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.

Language: Английский

Citations

20
Combining LoRA to GPT-Neo to Reduce Large Language Model Hallucination DOI Creative Commons

Shi-han Huang,

Chia-Yu Chen

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

Published: June 4, 2024

Abstract The deployment of Large Language Models (LLMs) often suffers from generating hallucinations, leading to outputs that appear plausible but are factually inaccurate or nonsensical. Incorporating Low-Rank Adaptation (LoRA) into GPT-Neo presents a novel approach mitigating these hallucinations by leveraging the efficiency low-rank approximations. This research details integration LoRA GPT-Neo, demonstrating significant improvements in predictive performance, factual accuracy, and reduction hallucination rates. augmented model shows enhanced robustness efficiency, making it more suitable for applications requiring high accuracy reliability. Through comprehensive evaluations involving perplexity, BLEU, ROUGE-L scores, qualitative analysis, study highlights model's ability generate coherent contextually appropriate text. findings demonstrate potential transform LLM reducing computational complexity memory footprint, thus facilitating use large-scale models resource-constrained environments. advancement opens new possibilities across various domains, ensuring coherence generated content.

Language: Английский

Citations

15
Dynamic Supplementation of Federated Search Results for Reducing Hallucinations in LLMs DOI Open Access
Jichang Chen,

Xinnan Huang,

Yongping Li

et al.

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

Citations

12
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
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
Improving Generalization Beyond Training Data with Compositional Generalization in Large Language Models DOI Open Access

Wong Ho-tin,

Gar-lai Yip

Published: May 20, 2024

Enhancing compositional generalization in language models addresses a crucial challenge natural processing, significantly improving their ability to understand and generate novel combinations of known concepts. The investigation utilized the Mistral 7x8B model, employing advanced data augmentation refined training methodologies enhance performance. By incorporating diverse challenging compositions during training, model demonstrated substantial gains standard evaluation metrics, including accuracy, precision, recall, F1-score. Specialized metrics such as accuracy contextual coherence also showed marked improvement, reflecting model's enhanced capacity correct contextually relevant outputs when faced with compositions. study further highlighted significant reduction hallucination rates, underscoring increased logical consistency factual accuracy. This was statistically significant, indicating robust enhancement Qualitative analysis corroborated these findings, revealing more coherent narratives accurate information retrieval generated responses. These improvements are particularly important for real-world applications where reliability appropriateness essential. comprehensive effectiveness proposed techniques, providing valuable insights into underlying mechanisms that contribute improved findings underscore importance iterative experimentation validation refining architectures techniques. advancing capabilities models, this research contributes development robust, flexible, reliable AI systems capable handling broader range linguistic tasks greater understanding.

Language: Английский

Citations

8
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
A Comparative Analysis of Cultural Alignment in Large Language Models in Bilingual Contexts DOI Open Access

Ximen Yuan,

Jinshan Hu, Qian Zhang

et al.

Published: June 10, 2024

Artificial intelligence (AI) systems, particularly those capable of natural language processing, are increasingly becoming integral to diverse aspects human life and interaction. Understanding the cultural biases embedded within AI, especially in how it aligns with specific values, is crucial for ensuring its effective equitable deployment. This research examines alignment AI-generated responses mainstream Chinese such as Confucian harmony, Daoist balance, collectivism, respect authority, family-centric principles. By analyzing both English, study highlights discrepancies inherent AI offering valuable insights into their implications development. The findings reveal that while demonstrates general significant variations exist between contexts, emphasizing importance linguistic specificity interactions. Quantitative metrics thematic analyses demonstrate necessity culturally aware contributing broader discourse on ethical development providing guidance creating more inclusive adaptable systems.

Language: Английский

Citations

4
Dynamic Moving Target Defense for Mitigating Targeted LLM Prompt Injection DOI Creative Commons

Samuel Panterino,

Matthew Fellington

Published: June 12, 2024

The increasing sophistication and capabilities of artificial intelligence systems have brought about significant advancements in natural language processing, yet they also exposed these to various security vulnerabilities, particularly targeted prompt injection attacks. introduction a moving target defence mechanism offers novel approach mitigating attacks through continuously altering the model’s parameters configurations, thereby creating an unpredictable environment that complicates adversarial efforts. This research provides comprehensive evaluation mechanism, detailing selection categorization attacks, development dynamic techniques such as random parameter perturbation, model re-initialization, context adjustments, their seamless integration with Mistral LLM. experimental results indicate substantial reduction attack success rate, maintaining high performance metrics while managing computational overhead efficiently. findings highlight practical applicability potential for widespread adoption enhancing resilience large models against sophisticated tactics.

Language: Английский

Citations

4