Dynamic Moving Target Defense for Mitigating Targeted LLM Prompt Injection

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

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Mitigating Hallucinations in Large Language Models with Sliding Generation and Self-Checks

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

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Large Language Model Understands Chinese Better with Mega Tokenization

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

Published: June 10, 2024

The rapid evolution of natural language processing has seen significant advancements in models, particularly for languages with simpler orthographies. However, challenges persist accurately and understanding complex morphological structures, such as Chinese, due to the limitations traditional tokenization methods. Introducing mega tokenization, which involves significantly larger tokens, represents a novel transformative approach that enhances semantic preservation contextual coherence sophisticated character sequences. study compares performance an adapted model against standard model, demonstrating substantial improvements across tasks machine translation, text summarisation, question answering. Through rigorous evaluation statistical analysis, shows superior metrics, indicating effectiveness addressing unique posed by Chinese language. implications this extend various applications, underscoring its potential revolutionise multilingual high-stakes environments. Future research directions are proposed further optimise expand applicability diverse linguistic contexts.

Language: Английский

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Assessing Audio Hallucination in Large Multimodal Models

Published: June 10, 2024

Speech recognition systems have become increasingly integral in various applications, from virtual assistants to automated transcription services, necessitating the development of models capable accurately processing and transcribing spoken language. The introduction multimodal like ChatGPT-4 Gemini 1.5 Flash represents a significant advancement this field, yet challenges such as audio hallucination, pronunciation handling, punctuation placement remain critical hurdles. This study provides comprehensive evaluation Flash, focusing on their performance English inputs under varying conditions. By employing rigorous statistical qualitative analysis, including metrics Word Error Rate (WER) Character (CER), reveals that exhibits superior accuracy reliability handling complex speech patterns. Detailed examination further elucidates specific areas where each model excels or faces challenges. findings demonstrate importance continuous refinement enhancement improve practical applicability real-world scenarios. research contributes valuable insights into strengths limitations leading technologies, providing benchmark for future developments field.

Language: Английский

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Assessing Reasoning Capabilities of Commercial LLMs: A Comparative Study of Inductive and Deductive Tasks

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

Published: Aug. 6, 2024

Artificial intelligence has revolutionized various fields through its ability to process and generate human-like text, leading significant advancements in tasks requiring language comprehension generation. However, the evaluation of fundamental reasoning abilities within commercial LLMs, specifically inductive deductive reasoning, remains crucial for understanding their cognitive capabilities limitations. This research provides a comprehensive assessment ChatGPT, Gemini, Claude, using meticulously designed set evaluate performance. The methodology involved selection diverse datasets, design complex tasks, implementation robust automated testing framework. Statistical analyses, including ANOVA regression techniques, were employed rigorously compare models’ performance across different tasks. Results indicated that ChatGPT consistently outperformed other models, particularly excelling high precision recall, while Gemini Claude exhibited variability capabilities. study highlights strengths weaknesses each model, offering insights into relative potential areas improvement. Implications AI development are significant, emphasizing need tailored model designs continued innovation training techniques enhance abilities. contributes broader providing foundation future developing more capable reliable intelligent systems.

Language: Английский

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Assessing the ability of GPT-4o to visually recognize medications and provide patient education

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Nov. 5, 2024

Various studies have investigated the ability of ChatGPT (OpenAI) to provide medication information; however, a new promising feature has now been added, which allows visual input and is yet be evaluated. Here, we aimed qualitatively assess its visually recognize medications, through picture input, patient education via written output. The responses were evaluated by accuracy, precision clarity using 4-point Likert-like scale. In regards handling providing responses, GPT-4o was able all 20 tested medications from packaging pictures, even with blurring, retrieve their active ingredients, identify formulations dosage forms detailed, concise enough, in an almost completely accurate, precise clear manner score 3.55 ± 0.605 (85%). contrast, output generated images illustrating usage instructions contained many errors that would either hinder effectiveness or cause direct harm poor 1.5 0.577 (16.7%). conclusion, capable identifying pictures exhibits contrasting performance between very impressive scores, respectively.

Language: Английский

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Dynamic Contextual Alignment Mechanisms for Improving the Internal Representational Consistency in Large Language Models

Published: Nov. 18, 2024

The increasing complexity of language models naturally demands innovative approaches to maintain internal representational consistency. This paper introduces Dynamic Contextual Alignment Mechanisms, a novel framework designed enhance semantic coherence within large models. By integrating adaptive recalibration strategies, the proposed mechanism aligns intermediate representations across multiple layers, thereby reducing contextual ambiguities and improving interpretative processes Comprehensive evaluations demonstrate significant reductions in perplexity attention entropy, alongside improvements scores, indicating mechanism's efficacy refining understanding. Comparative analyses reveal that, unlike traditional methods relying on fine-tuning or auxiliary this approach inherently enhances alignment without substantial computational overhead. findings potential Mechanisms advance robustness adaptability diverse applications, addressing fundamental challenges setting foundation for future developments field.

Language: Английский

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Dynamic Neural Embedding for Contextual Regeneration in Large Language Models

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

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Adaptive Neural Contextualization for Expansive Knowledge Representation

Published: Nov. 25, 2024

Adaptive approaches to context modeling have emerged as critical mechanisms for addressing the limitations of static representation techniques, particularly in tasks requiring complex understanding linguistic dependencies. The proposed framework introduces a dynamic contextualization mechanism that enhances representational capabilities transformer-based architectures through iterative refinement context-sensitive embeddings. Quantitative evaluations demonstrated significant improvements accuracy, contextual coherence, and perplexity reduction across multiple benchmarks, establishing robustness approach under diverse input conditions. Qualitative assessments highlighted framework's ability maintain semantic alignment domain-specific tasks, within highly specialized or noisy datasets. methodology incorporated adaptive layers seamlessly into an open-source transformer model, enabling efficient long-sequence processing without imposing excessive computational demands. Cross-lingual further validated its capacity generalize effectively typologically languages, highlighting potential multilingual applications. integration hierarchical attention facilitated capture long-range dependencies, while cross-attention modules ensured precise with task-specific queries. Results also robust performance adversarial scenarios, showcasing adaptability unstructured incomplete inputs. Memory utilization analyses revealed maintained scalability large datasets, balancing efficiency enhanced metrics. redefines boundaries dynamically adjust representations, offering scalable solution challenges. These findings establish Neural Contextualization foundational innovation addresses gaps current methodologies advancing field language efficiency.

Language: Английский

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