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

Assessing Visual Hallucinations in Vision-Enabled Large Language Models DOI Creative Commons
Pingping Lu, Liang Huang, Wen Tan

et al.

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

Published: May 9, 2024

Abstract Recent advancements in vision-enabled large language models have prompted a renewed interest evaluating their capabilities and limitations when interpreting complex visual data. The current research employs ImageNet-A, dataset specifically designed with adversarially selected images that challenge standard AI models, to test the processing robustness of three prominent models: GPT-4 Vision, Google Gemini 1.5, Anthropic Claude 3. Quantitative analyses revealed notable disparities misclassification rates types errors among these indicating variation ability handle adversarial inputs effectively. Vision demonstrated commendable robustness, whereas 1.5 excelled speed efficiency. 3, while showing intermediate accuracy levels, displayed significant propensity for contextual misinterpretations. Qualitative evaluations further assessed relevance plausibility models' hallucinations, uncovering challenges achieving human-like understanding ambiguous or scenes. findings emphasize necessity improvements semantic understanding. Future directions include enhancing refining evaluation metrics better capture qualitative aspects understanding, fostering interdisciplinary collaborations develop systems more nuanced interpretive abilities. study underscores ongoing journey towards can match human perceptual skills, highlighting both progress made considerable remain.

Language: Английский

Citations

24
Knowledge Accuracy and Reducing Hallucinations in LLMs via Dynamic Domain Knowledge Injection DOI Creative Commons

Roman Capellini,

Frank Atienza,

Melanie Sconfield

et al.

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

Published: June 7, 2024

Abstract Natural language processing has seen substantial progress with the development of highly sophisticated models capable understanding and generating human-like text. However, a persistent challenge remains in enhancing accuracy these when dealing domain-specific knowledge, particularly avoiding hallucinations or plausible but incorrect information. The dynamic domain knowledge injection mechanism introduced this research represents significant advancement by allowing continuous integration prioritisation specialised information, thereby improving model's performance reliability. By dynamically adjusting hidden weights GPT-Neo based on relevance accuracy, modified model achieved higher precision, recall, F1-scores, exhibited reduced hallucination rates across diverse domains such as cybersecurity, medical financial data, legal documents. A comprehensive evaluation framework, including benchmark creation metrics, validated effectiveness approach, demonstrating that can substantially enhance utility large fields. results highlight transformative potential method, offering robust pathway for more accurate contextually aware models. Detailed analysis ablation studies further elucidate contributions each component within modification process, providing critical insights into optimisation future applications innovative approach.

Language: Английский

Citations

10
Measuring the Visual Hallucination in ChatGPT on Visually Deceptive Images DOI Open Access

Linzhi Ping,

Yue Gu,

Liefeng Feng

et al.

Published: May 28, 2024

The evaluation of visual hallucinations in multimodal AI models is novel and significant because it addresses a critical gap understanding how systems interpret deceptive inputs. study systematically assessed ChatGPT's performance on synthetic dataset visually non-deceptive images, employing both quantitative qualitative analysis. Results revealed that while ChatGPT achieved high accuracy standard recognition tasks, its diminished when faced with highlighting areas for further improvement. analysis provided insights into the model's underlying mechanisms, such as extensive pretraining sophisticated integration capabilities, which contribute to robustness against deceptions. study's findings have important implications development more reliable robust technologies, offering benchmark future evaluations practical guidelines enhancing systems.

Language: Английский

Citations

9
Measuring the Perceived IQ of Multimodal Large Language Models Using Standardized IQ Tests DOI Creative Commons
Eryk Wasilewski, Mirek Jablonski

Published: May 13, 2024

Evaluating the intelligence of multimodal large language models (LLMs) using adapted human IQ tests poses unique challenges and opportunities for understanding AI capabilities.By applying Wechsler Adult Intelligence Scale (WAIS), customized to assess cognitive functions LLMs such as Baidu Benie, Google Gemini, Anthropic Claude, significant insights into complex intellectual landscape these systems were revealed.The study demonstrates that can exhibit sophisticated abilities, performing tasks requiring advanced verbal comprehension, perceptual reasoning, problemsolving-traditionally considered within purview cognition.The research also highlights distinct profiles each model, reflecting their specialized architectures training.However, acknowledges inherent limitations in human-oriented assessment, emphasizing need ongoing refinement testing methodologies keep pace with development.Future directions include creation dynamic adaptive frameworks better align capabilities evolving systems, ensuring integration societal remains aligned values safety standards.

Language: Английский

Citations

6
Industrial applications of large language models DOI Creative Commons
Mubashar Raza,

Zarmina Jahangir,

Muhammad Bilal Riaz

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 21, 2025

Large language models (LLMs) are artificial intelligence (AI) based computational designed to understand and generate human like text. With billions of training parameters, LLMs excel in identifying intricate patterns, enabling remarkable performance across a variety natural processing (NLP) tasks. After the introduction transformer architectures, they impacting industry with their text generation capabilities. play an innovative role various industries by automating NLP In healthcare, assist diagnosing diseases, personalizing treatment plans, managing patient data. provide predictive maintenance automotive industry. recommendation systems, consumer behavior analyzers. facilitates researchers offer personalized learning experiences education. finance banking, used for fraud detection, customer service automation, risk management. driving significant advancements tasks, improving accuracy, providing deeper insights. Despite these advancements, face challenges such as ethical concerns, biases data, resource requirements, which must be addressed ensure impartial sustainable deployment. This study provides comprehensive analysis LLMs, evolution, diverse applications industries, offering valuable insights into transformative potential accompanying limitations.

Language: Английский

Citations

0
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
Automated Comparative Analysis of Visual and Textual Representations of Logographic Writing Systems in Large Language Models DOI Creative Commons

Peng Shao,

Ruichen Li,

Kai Qian

et al.

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

Published: Aug. 16, 2024

Abstract The complex nature of logographic writing systems, characterized by their visually intricate characters and context-dependent meanings, presents unique challenges for computational models designed primarily alphabetic scripts. Understanding the ability LLMs to process scripts across visual textual input modalities is essential advancing application in multilingual contexts. novel approach presented this study systematically compares performance when interpreting as both data, offering new insights into semantic consistency accuracy model outputs these modalities. findings reveal critical disparities performance, particularly highlighting models' tendency favor inputs, which suggests need further refinement multimodal processing capabilities. Through detailed analysis error patterns, similarity, complexity, research demonstrates importance developing more robust versatile LLM architectures capable effectively managing inherent complexities systems. conclusions drawn from not only provide a deeper understanding limitations current but also set stage future innovations field, aiming enhance generalize diverse linguistic structures types.

Language: Английский

Citations

3
Game-Theoretic Approaches for Step-wise Controllable Text Generation in Large Language Models DOI

Daniel Sefeni,

Michael Johnson,

Joshua Lee

et al.

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

Published: Sept. 3, 2024

The growing reliance on AI-generated content across various industries necessitates robust methods for controlling the outputs of language models to ensure quality, relevance, and adherence ethical guidelines.Introducing a novel gametheoretic framework, this research establishes structured approach controllable text generation, enabling strategic manipulation model through adaptive prompt interventions.The study employed Mistral model, utilizing concepts Nash equilibrium feedback loops dynamically adjust strategies, optimizing balance between alignment, diversity, coherence.Experimental results demonstrated that different strategies distinctly influenced generated text, with direct prompts enhancing relevance interrogative promoting creative expression.Case studies further illustrated practical applications showcasing its adaptability generation tasks.The comparative analysis against traditional control highlighted superiority game-theoretic in achieving high-quality, controlled outputs.These findings demonstrate framework's potential enhance AIdriven offering significant implications human-AI collaboration, automated creation, deployment AI technologies.

Language: Английский

Citations

3
Measuring the IQ of Mainstream Large Language Models in Chinese Using the Wechsler Adult Intelligence Scale DOI Creative Commons

Jingjing Huang,

Ou Li

Published: June 7, 2024

Artificial intelligence continues to revolutionize various domains, with large language models (LLMs) pushing the boundaries of what machines can understand and generate. Evaluating intellectual linguistic capabilities LLMs using standardized tests like Wechsler Adult Intelligence Scale (WAIS) provides a novel significant approach understanding their cognitive strengths limitations. This research presents comprehensive evaluation Baidu Ernie OpenAI ChatGPT, comparing performance in IQ Chinese tasks. The assessments revealed that ChatGPT achieved marginally higher composite score, excelling particularly verbal comprehension working memory. demonstrated superior cultural appropriateness accuracy, reflecting its strong alignment context. study involved translating WAIS into Chinese, integrating multimodal inputs, applying rigorous statistical analyses ensure robust reliable results. findings demonstrate distinct each model, showing versatility handling diverse textual data culturally relevant grammatically precise responses. implications for future development emphasize importance contextually training integration enhance performance. framework offers valuable insights advancing artificial intelligence, guiding towards more intelligent, adaptable, aware models.

Language: Английский

Citations

2
Automated Learning of Fine-Grained Citation Patterns in Open Source Large Language Models DOI Open Access
Edward Harcourt,

James Loxley,

Benjamin Stanson

et al.

Published: Aug. 14, 2024

In academic writing, citations play an essential role in ensuring the attribution of ideas, supporting scholarly claims, and enabling traceability knowledge across disciplines. However, manual process citation generation is often time-consuming prone to errors, leading inconsistencies that can undermine credibility work. The novel approach explored this study leverages advanced machine learning techniques automate process, offering a significant improvement both accuracy efficiency. Through integration contextual semantic features, model demonstrates superior ability replicate complex patterns, adapt various disciplines, generate contextually appropriate with high precision. results rigorous experiments reveal not only outperforms traditional tools but also exhibits robust scalability, making it well-suited for large-scale applications. This research contributes field automated providing powerful tool enhances quality integrity communication.

Language: Английский

Citations

1