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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Bridging Today and the Future of Humanity: AI Safety in 2024 and Beyond

Published: Feb. 25, 2025

The advancements in generative AI inevitably raise concerns about their risks and safety implications, which, return, catalyzes significant progress safety. However, as this field continues to evolve, a critical question arises: are our current efforts on aligned with the of well long-term goal human civilization? This paper presents blueprint for an advanced society leverages vision guide efforts. It outlines future where _Internet Everything_ becomes reality, creates roadmap technological towards envisioned future. For each stage advancements, forecasts potential issues that humanity may face. By projecting against blueprint, examines alignment between needs, highlights unique challenges missions demand increasing attention from practitioners 2020s. aims offer broader perspective safety, emphasizing should not only address immediate but also anticipate expanding landscape, thereby promoting safe sustainable civilization.

Language: Английский

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A CIA Triad-Based Taxonomy of Prompt Attacks on Large Language Models

Future Internet, Journal Year: 2025, Volume and Issue: 17(3), P. 113 - 113

Published: March 3, 2025

The rapid proliferation of Large Language Models (LLMs) across industries such as healthcare, finance, and legal services has revolutionized modern applications. However, their increasing adoption exposes critical vulnerabilities, particularly through adversarial prompt attacks that compromise LLM security. These prompt-based exploit weaknesses in LLMs to manipulate outputs, leading breaches confidentiality, corruption integrity, disruption availability. Despite significance, existing research lacks a comprehensive framework systematically understand mitigate these threats. This paper addresses this gap by introducing taxonomy based on the Confidentiality, Integrity, Availability (CIA) triad, an important cornerstone cybersecurity. structured lays foundation for unique security engineering, which is essential identifying risks, understanding mechanisms, devising targeted protocols. By bridging knowledge gap, present study provides actionable insights can enhance resilience ensure secure deployment high-stakes real-world environments.

Language: Английский

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Developer and LLM Pair Programming: An Empirical Study of Role Dynamics and Prompt-Based Collaboration

International Journal of Advanced Research in Science Communication and Technology, Journal Year: 2025, Volume and Issue: unknown, P. 436 - 444

Published: May 12, 2025

With the introduction of large language models (LLMs) as coding partners, classic pair programming dynamics are being rewritten. This research empirically examines collaboration between software developers and LLMs on tasks, uncovering a dynamic role toggling informed by prompt accuracy contextual cues. Instead deterministic driver-navigator dichotomies, we find an emergent interdependence where programmers function orchestrators intent oscillate executor, interpreter, creative collaborator. Prompt design has emerged critical skill for orchestrating collaboration, shifting focus from code authorship to dialogical problem-solving. perspective introduces new vision human-AI co-creation in coding, highlighting its potential within future intelligent development environments.

Language: Английский

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Enhancing Inference Efficiency and Accuracy in Large Language Models through Next-Phrase Prediction

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

Published: Aug. 7, 2024

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

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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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Optimizing LLM Inference Clusters for Enhanced Performance and Energy Efficiency

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

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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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Automated Learning of Fine-Grained Citation Patterns in Open Source Large Language Models

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

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

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

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