Enhancing Inference Efficiency in Large Language Models through Rapid Feed-Forward Information Propagation

Опубликована: Июнь 13, 2024

The increasing complexity and computational demands of language models require innovations to enhance their efficiency performance. novel approach rapid feed-forward information propagation presents significant advancements by optimizing the architecture Mistral Large model, leading substantial improvements in inference speed memory usage. Comprehensive architectural modifications, including parameter sharing reduced layer depth, streamlined model's processes, while integration additional pathways mixed-precision training further optimized its efficiency. Detailed experimental results demonstrate effectiveness these enhancements, showing marked latency, throughput, accuracy across various benchmark datasets. study also highlights robustness scalability, ensuring reliable performance diverse deployment scenarios. implications findings are profound, providing a framework for developing more efficient, scalable, high-performing models, with broad applicability real-world natural processing tasks.

Язык: Английский

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Dynamic Moving Target Defense for Mitigating Targeted LLM Prompt Injection

Опубликована: Июнь 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.

Язык: Английский

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A Comparative Study of Cultural Hallucination in Large Language Models on Culturally Specific Ethical Questions

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

Опубликована: Июнь 12, 2024

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.

Язык: Английский

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

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

Опубликована: Июнь 11, 2024

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.

Язык: Английский

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Efficient Conceptual Knowledge Removal in Large Language Models: Methods and Evaluations

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

Опубликована: Окт. 8, 2024

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.

Язык: Английский

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Adaptive Neural Contextualization for Expansive Knowledge Representation

Опубликована: Ноя. 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.

Язык: Английский

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Quantitative Analysis of the Relationship Between Optimal Learning Rate and Batch Size Scaling in Large Language Models

Опубликована: Июнь 13, 2024

The rapid development of natural language processing has led to the emergence sophisticated models capable performing a wide array tasks with human-like proficiency. Identifying optimal relationship between learning rate and batch size is crucial for enhancing efficiency effectiveness training these models. Through systematic experimentation such as Baidu Ernie, Meta Llama, Moonshot Kimi, this research demonstrates linear hyperparameters, providing practical framework their adjustment. Results indicate that appropriate scaling rates sizes can significantly improve efficiency, model accuracy, convergence time. findings offer valuable insights into dynamics training, presenting scalable approach reduce computational costs enhance robustness, thereby contributing broader field artificial intelligence.

Язык: Английский

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Growing Smaller Language Models Using Knowledge Distillation from Larger Models

Опубликована: Июнь 25, 2024

The rapid development of natural language processing technologies has necessitated models that are both high-performing and computationally efficient, posing a challenge for resource-constrained environments. Knowledge distillation, technique where smaller model learns from larger pre-trained model, offers novel significant solution by enhancing the capabilities while maintaining reduced computational footprint. This research explores application knowledge distillation to finetune GPT-Neo using Mistral Large, resulting in notable improvements accuracy, precision, recall, F1-score across tasks such as text generation, translation, summarization, question-answering. Comprehensive evaluations demonstrated substantial reductions inference time, memory usage, energy consumption, highlighting practical benefits approach. finetuned exhibited enhanced linguistic proficiency, coherence, fluency, contextual underscoring effectiveness optimizing performance. findings validate robust method advancing technologies, ensuring high performance environments with limited resources.

Язык: Английский

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Enhancements to Large Language Models: Introducing Dynamic Syntactic Insertion for Improved Model Robustness and Generalization

Authorea (Authorea), Год журнала: 2024, Номер unknown

Опубликована: Окт. 14, 2024

The growing complexity and scale of modern deep learning models have improved the ability to generate understand human language, yet challenges persist in achieving robust generalization syntactic flexibility.Dynamic Syntactic Insertion (DSI) addresses these limitations through novel introduction random variations during finetuning phase, enhancing model's capacity process diverse linguistic structures.Through empirical experiments on GPT-NeoX architecture, significant performance improvements were observed across multiple metrics, including robustness, fluency, accuracy.The DSI-enhanced model consistently outperformed baseline, particularly handling syntactically complex perturbed datasets, demonstrating its adaptability a broader range inputs.Furthermore, incorporation variability led reductions perplexity increased tasks GLUE benchmark, highlighting method's effectiveness.The findings from this study suggest that augmentation techniques, such as DSI, provide promising pathway for improving resilience language environments.

Язык: Английский

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