Combining LoRA to GPT-Neo to Reduce Large Language Model Hallucination

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

Опубликована: Июнь 4, 2024

The deployment of Large Language Models (LLMs) often suffers from generating hallucinations, leading to outputs that appear plausible but are factually inaccurate or nonsensical. Incorporating Low-Rank Adaptation (LoRA) into GPT-Neo presents a novel approach mitigating these hallucinations by leveraging the efficiency low-rank approximations. This research details integration LoRA GPT-Neo, demonstrating significant improvements in predictive performance, factual accuracy, and reduction hallucination rates. augmented model shows enhanced robustness efficiency, making it more suitable for applications requiring high accuracy reliability. Through comprehensive evaluations involving perplexity, BLEU, ROUGE-L scores, qualitative analysis, study highlights model's ability generate coherent contextually appropriate text. findings demonstrate potential transform LLM reducing computational complexity memory footprint, thus facilitating use large-scale models resource-constrained environments. advancement opens new possibilities across various domains, ensuring coherence generated content.

Язык: Английский

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Dynamic Supplementation of Federated Search Results for Reducing Hallucinations in LLMs

Опубликована: Июнь 6, 2024

The increasing use of AI-generated content has highlighted the critical issue hallucinations, where models produce factually incorrect or misleading outputs. Addressing this challenge, a novel approach dynamically supplements federated search engine results in real-time to significantly reduce hallucinations and enhance response accuracy. methodology involves integrating data from multiple engines into responses generated by Mistral Large model, thereby providing more accurate contextually appropriate output. Comprehensive evaluation using Microsoft PromptBench dataset demonstrates substantial improvements accuracy, relevance, reduction hallucinations. Quantitative performance metrics, statistical analysis, detailed case studies confirm effectiveness dynamic supplementation approach. findings suggest significant implications for developing reliable AI applications across various domains, emphasizing potential hybrid systems that combine strengths large language information retrieval. Future research directions include refining triggering mechanisms, expanding sources, optimizing process further scalability.

Язык: Английский

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Investigating Hallucination Tendencies of Large Language Models in Japanese and English

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

Опубликована: Июнь 4, 2024

The increasing reliance on artificial intelligence for natural language processing has brought to light the issue of hallucinations in models, where models generate content that appears plausible but is factually incorrect. Exploring comparative hallucination tendencies Japanese and English reveals significant differences, highlighting importance understanding language-specific challenges model performance. A rigorous methodology was employed quantify frequency severity hallucinations, with comprehensive data collection from diverse sources both languages. Quantitative analysis indicated a higher propensity responses, attributed complex syntactical contextual structures language. Qualitative examples provided concrete illustrations errors encountered, demonstrating impact linguistic cultural factors. findings emphasize necessity more linguistically contextually rich training datasets, along advanced fact-checking mechanisms, improve reliability models. study's implications extend development tailored strategies enhancing accuracy across different languages, contributing broader goal creating robust trustworthy systems global applications.

Язык: Английский

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Knowledge Accuracy and Reducing Hallucinations in LLMs via Dynamic Domain Knowledge Injection

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

Опубликована: Июнь 7, 2024

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.

Язык: Английский

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Optimizing Knowledge Extraction in Large Language Models Using Dynamic Tokenization Dictionaries

Опубликована: Июнь 11, 2024

Tokenization methods have long been a critical component in the performance of language models, yet traditional static approaches often fall short capturing dynamic nature language. The novel concept implementing tokenization dictionary within Llama model presents significant advancement, offering real-time adaptability response to evolving linguistic patterns. adaptive algorithm continuously updates token set based on frequency and context, thereby enhancing model's ability generate coherent contextually relevant outputs. Comprehensive evaluation across multiple benchmark datasets reveals substantial improvements metrics such as perplexity, F1 Score, BLEU ROUGE underscoring efficacy tokenization. implications these findings extend various domains, including healthcare, legal analysis, education, customer service, demonstrating broad applicability transformative potential tokenized dictionaries. This research not only advances understanding processes but also provides robust framework for efficiency accuracy large models real-world applications.

Язык: Английский

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Automated Methodologies for Evaluating Lying, Hallucinations, and Bias in Large Language Models

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

Опубликована: Авг. 6, 2024

As large language models become integral to various applications, ensuring the reliability and impartiality of their outputs is paramount importance. The proposed methodologies for evaluating truthfulness, hallucinations, bias in AI represent a significant advancement, offering an automated objective approach validation without human intervention. Automated fact-checking systems, synthetic datasets, consistency analysis, detection algorithms were integrated provide comprehensive evaluation framework. Results from these experiments indicated high accuracy identifying truthful information, robust discernment true versus false statements, stable performance across diverse scenarios, effective mitigation biases. These findings highlight potential enhancing fairness, contributing development more trustworthy systems. Future research directions include expanding reference databases, refining improving techniques further enhance model evaluations.

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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Benchmarking Open-Source Large Language Models on Code-Switched Tagalog-English Retrieval Augmented Generation

Journal of Advances in Information Technology, Год журнала: 2025, Номер 16(2), С. 233 - 242

Опубликована: Янв. 1, 2025

Язык: Английский

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Large Language Model Understands Chinese Better with Mega Tokenization

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

Опубликована: Июнь 10, 2024

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.

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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