Boosting Long-term Factuality in Large Language Model with Real-World Entity Queries

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

Published: Aug. 2, 2024

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

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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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Understanding and Detecting File Knowledge Leakage in GPT App Ecosystem

Published: April 22, 2025

Language: Английский

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Gradual Improvement of Contextual Understanding in Large Language Models via Reverse Prompt Engineering

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

Published: Aug. 15, 2024

The increasing demand for more sophisticated and contextually aware language generation has highlighted the limitations of traditional models, which often struggle to maintain relevance accuracy across diverse dynamic contexts. novel concept reverse prompt engineering, introduced in this research, represents a significant breakthrough by enabling prompts that are retrospectively aligned with desired outputs, thereby enhancing model's ability adapt varying contexts precision. Through fine-tuning Mistral model, combined integration research achieved substantial improvements context-specific generation, demonstrating enhanced performance wide range tasks, including summarization, translation, question answering. results demonstrate importance modeling adaptive together contribute accurate relevant output, offering robust framework future advancements model development. methodologies developed study not only advance current understanding context adaptation models but also pave way versatile scalable applications various domains.

Language: Английский

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Evaluating Large Language Models through the Lens of Linguistic Proficiency and World Knowledge: A Comparative Study

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

Published: Aug. 27, 2024

The development of sophisticated artificial intelligence systems has rapidly transformed various industries, creating an increased demand for models capable advanced linguistic processing and comprehensive knowledge integration.Addressing this demand, the presented evaluation explores capabilities ChatGPT Google Gemini through a dual lens skill world knowledge, offering unique perspective that goes beyond traditional assessments focused solely on language generation or factual recall.Through carefully structured methodology, which incorporates range tasks designed to test syntax, grammar, vocabulary, logical reasoning, study provides comparative analysis how well each model can manage both complexity retrieval application information.Results indicate excels in maintaining grammatical accuracy consistency, making it particularly suitable applications requiring rigorous precision, while demonstrates superior contextual comprehension reasoning abilities, suggesting its efficacy scenarios where complex understanding ability integrate diverse are crucial.The insights derived from not only highlight current limitations but also provide foundational inform future developments enhancing management within AI systems.

Language: Английский

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Assessing the Ineffectiveness of Synthetic Reinforcement Learning Feedback in Fine-Tuning Large Language Models

Published: Aug. 6, 2024

The rapid evolution of artificial intelligence has brought significant advancements in various applications, yet fine-tuning models to align outputs with user needs and ethical standards remains a challenging endeavor. Introducing synthetic reinforcement learning feedback provides novel scalable approach this challenge, bypassing the logistical financial burdens human evaluators. Through comprehensive experimentation open-source Llama model, improvements were observed performance metrics such as coherence, relevance, informativeness, factual accuracy, demonstrating efficacy mechanisms. study's methodology involved leveraging automated reward metrics, iterative parameter updates, sophisticated optimization techniques, culminating robust framework for model fine-tuning. Statistical validation demonstrated reliability improvements, while detailed analysis highlighted both potential limitations systems. findings offer substantial contributions field, providing replicable blueprint future research practical insights into optimization. implications large-scale deployments AI systems are profound, suggesting that mechanisms can significantly enhance adaptability language applications.

Language: Английский

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Effects of Adaptive Feedback Generated by a Large Language Model: A Case Study in Teacher Education

Computers and Education Artificial Intelligence, Journal Year: 2024, Volume and Issue: unknown, P. 100349 - 100349

Published: Dec. 1, 2024

Language: Английский

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Growing Smaller Language Models Using Knowledge Distillation from Larger Models

Published: June 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.

Language: Английский

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Enhancing Contextual Understanding in Large Language Models with Dynamic Dependency Structures: A Methodological Approach

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

Published: July 30, 2024

The sophisticated machine learning models transformed the ability to understand and generate human language, yet challenges remain in maintaining contextual coherence relevance over extended sequences. Introducing dynamic dependency structures into GPT-Neo represents a significant advancement, enabling real-time adaptation of syntactic relationships based on evolving context, thereby enhancing model's performance generating contextually appropriate coherent text. integration context-aware updater reinforcement techniques has demonstrated substantial improvements both quantitative metrics such as perplexity BLEU scores qualitative evaluations. This research details implementation evaluation modified model, showcasing its superior capabilities tasks like translation text summarization. findings highlight potential address limitations traditional fixed frameworks, offering robust methodological advancement for more language modeling. By capture complex relevant information, proposed approach paves way development advanced AI systems capable performing processing with greater accuracy fluency.

Language: Английский

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Probabilistic Neural Interactions for Dynamic Context Understanding in Large Language Models

Published: Nov. 18, 2024

The exponential growth in data complexity and volume requires the development of more sophisticated language models capable understanding generating human-like text. Introducing Probabilistic Neural Interactions (PNI) offers a novel approach that enhances dynamic context comprehension through probabilistic mechanisms within neural architectures. This study presents integration PNI into an open-source large model, detailing implementation framework mathematical formulations. Experimental evaluations demonstrate significant improvements model performance metrics, including accuracy adaptability, when compared to baseline models. Additionally, PNI-enhanced exhibits robustness noisy inputs scalability across various sizes, albeit with increased computational resource requirements. These findings suggest contributes advancement models, facilitating complex contextually appropriate processing capabilities.

Language: Английский

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