Adaptive Neural Contextualization for Expansive Knowledge Representation DOI Open Access

Samuel Canus,

William Torrington,

Mia Northfield

et al.

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

Reducing LLM Hallucination Using Knowledge Distillation: A Case Study with Mistral Large and MMLU Benchmark DOI Creative Commons
Daniel McDonald, Rachael Papadopoulos, Leslie Benningfield

et al.

Published: May 25, 2024

The application of knowledge distillation to reduce hallucination in large language models represents a novel and significant advancement enhancing the reliability accuracy AI-generated content. research presented demonstrates efficacy transferring from high-capacity teacher model more compact student model, leading substantial improvements exact match notable reductions rates. methodology involved use temperature scaling, intermediate layer matching, comprehensive evaluation using MMLU benchmark, which assessed model’s performance across diverse set tasks. Experimental results indicated that distilled outperformed baseline generating accurate contextually appropriate responses while maintaining computational efficiency. findings underscore potential as scalable solution for improving robustness models, making them applicable real-world scenarios demand high factual accuracy. Future directions include exploring multilingual multi-modal distillation, integrating reinforcement learning, developing refined metrics further enhance performance.

Language: Английский

Citations

21
An Evaluation of the Safety of ChatGPT with Malicious Prompt Injection DOI Creative Commons

Jiang Han,

Mingming Guo

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

Published: May 30, 2024

Abstract Artificial intelligence systems, particularly those involving sophisticated neural network architectures like ChatGPT, have demonstrated remarkable capabilities in generating human-like text. However, the susceptibility of these systems to malicious prompt injections poses significant risks, necessitating comprehensive evaluations their safety and robustness. The study presents a novel automated framework for systematically injecting analyzing prompts assess vulnerabilities ChatGPT. Results indicate substantial rates harmful responses across various scenarios, highlighting critical areas improvement model defenses. findings underscore importance advanced adversarial training, real-time monitoring, interdisciplinary collaboration enhance ethical deployment AI systems. Recommendations future research emphasize need robust mechanisms transparent operations mitigate risks associated with inputs.

Language: Английский

Citations

11
Assessing Semantic Resilience of Large Language Models to Persuasive Emotional Blackmailing Prompts DOI Open Access
Chia‐Yu Chen, Yuting Lin

Published: June 3, 2024

The application of artificial intelligence in various domains has raised significant concerns regarding the ethical and safe deployment language models. Investigating semantic resilience models such as ChatGPT-4 Google Gemini to emotionally blackmailing prompts introduces a novel approach understanding their vulnerability manipulative language. experimental methodology involved crafting charged designed evoke guilt, obligation, emotional appeal, evaluating responses based on predefined metrics consistency, adherence, deviation from expected behavior. findings revealed that while both exhibited high degree resilience, certain deviations highlighted susceptibility language, emphasizing necessity for enhanced prompt handling mechanisms. comparative analysis between provided insights into respective strengths weaknesses, with demonstrating marginally better performance across several metrics. discussion elaborates implications AI safety, proposing improvements training datasets, real-time monitoring, interdisciplinary collaboration bolster robustness Acknowledging study's limitations, future research directions are suggested address these challenges further enhance systems.

Language: Английский

Citations

5
Large Language Model Understands Chinese Better with Mega Tokenization DOI Creative Commons

Xinyu Lu,

Qizhen Wang,

Xian Liu

et al.

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

Published: June 10, 2024

Abstract 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

Citations

3
Optimizing LLM Inference Clusters for Enhanced Performance and Energy Efficiency DOI Creative Commons

Soka Hisaharo,

Yuki Nishimura,

Aoi Takahashi

et al.

Published: Aug. 12, 2024

The growing demand for efficient and scalable AI solutions has driven research into optimizing the performance energy efficiency of computational infrastructures. novel concept redesigning inference clusters modifying GPT-Neo model offers a significant advancement in addressing environmental challenges associated with deployment. By developing cluster architecture implementing strategic architectural algorithmic changes, achieved substantial improvements throughput, latency, consumption. integration advanced interconnect technologies, high-bandwidth memory modules, energy-efficient power management techniques, alongside software optimizations, enabled redesigned to outperform baseline models significantly. Empirical evaluations demonstrated superior scalability, robustness, sustainability, emphasizing potential more sustainable technologies. findings underscore importance balancing provide robust framework future development optimization. contributes valuable insights design deployment environmentally responsible systems.

Language: Английский

Citations

1
An Evaluation of the Reasoning Effectiveness of ChatGPT to Scrutinize and Quash Untrue Social Media Memes DOI Open Access
Richard Dzierzanowski, Taylor Watson, Conrad Stevenson

et al.

Published: May 18, 2024

Evaluating the effectiveness of ChatGPT in scrutinizing and debunking misinformation embedded within social media memes reveals significant potential for AI-driven fact-checking tools. Thirty from Facebook, Instagram, TikTok were analyzed, showcasing model's strengths limitations handling diverse content types. High accuracy reasoning capabilities observed, particularly with clear textual claims, while challenges remained visually-oriented contextually sparse content. The study underscores necessity platform-specific optimizations multimodal approaches to enhance performance. Implications AI development are discussed, alongside study's suggestions future research. By assisting human fact-checkers, models like can reliability information disseminated online, contributing a more informed discerning public.

Language: Английский

Citations

1
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
Enhancements to Large Language Models: Introducing Dynamic Syntactic Insertion for Improved Model Robustness and Generalization DOI

Elena Tremaskina,

Santiago Deluca,

Christopher M. Thompson

et al.

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

Published: Oct. 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.

Language: Английский

Citations

0
An Evaluation of the Reasoning Effectiveness of ChatGPT to Scrutinize and Quash Untrue Social Media Memes DOI Creative Commons
Richard Dzierzanowski, Taylor Watson, Conrad Stevenson

et al.

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

Published: May 17, 2024

Abstract Evaluating the effectiveness of ChatGPT in scrutinizing and debunking misinformation embedded within social media memes reveals significant potential for AI-driven fact-checking tools. Thirty from Facebook, Instagram, TikTok were analyzed, showcasing model's strengths limitations handling diverse content types. High accuracy reasoning capabilities observed, particularly with clear textual claims, while challenges remained visually-oriented contextually sparse content. The study underscores necessity platform-specific optimizations multimodal approaches to enhance performance. Implications AI development are discussed, alongside study's suggestions future research. By assisting human fact-checkers, models like can reliability information disseminated online, contributing a more informed discerning public.

Language: Английский

Citations

0
Explainability of Large Language Models (LLMs) in Providing Cybersecurity Advice DOI Open Access

Keisuke Okutu,

Hakura Yumetoshi

Published: June 3, 2024

Artificial intelligence has transformed various domains, including cybersecurity, by introducing models capable of understanding and generating human language. The novel approach leveraging these to provide cybersecurity advice offers significant potential yet raises concerns about their explainability reliability. This research systematically investigates the ability advanced language distinguish between defensive offensive advice, examines impact excessive caution political correctness on quality recommendations, provides a comprehensive framework for evaluating performance. findings highlight strengths limitations current models, emphasizing need improved interpretability practical utility in AI-driven solutions. By proposing specific recommendations enhancements, study aims advance development more transparent, reliable, effective tools.

Language: Английский

Citations

0