Reducing LLM Hallucination Using Knowledge Distillation: A Case Study with Mistral Large and MMLU Benchmark

Опубликована: Май 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.

Язык: Английский

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Efficiently Updating Domain Knowledge in Large Language Models: Techniques for Knowledge Injection without Comprehensive Retraining

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

Опубликована: Июнь 6, 2024

Recent advancements in natural language processing have highlighted the critical importance of efficiently updating pre-trained models with domain-specific knowledge. Traditional methods requiring comprehensive retraining are resource-intensive and impractical for many applications. The proposed techniques knowledge injection, including integration adapter layers, retrieval-augmented generation (RAG), distillation, offer a novel significant solution to this challenge by enabling efficient updates without extensive retraining. Adapter layers allow specialized fine-tuning, preserving model's original capabilities while incorporating new information. RAG enhances contextual relevance generated responses dynamically retrieving pertinent information from base. Knowledge distillation transfers smaller larger model, augmenting its performance domains. Experimental results demonstrated substantial improvements accuracy, precision, recall, F1-score, along enhanced coherence. findings demonstrate potential maintain accuracy dynamic, information-rich environments, making them particularly useful fields timely accurate

Язык: Английский

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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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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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Enhancing Contextual Understanding of Mistral LLM with External Knowledge Bases

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

Опубликована: Апрель 5, 2024

Abstract This study explores the enhancement of contextual understanding and factual accuracy in Language Learning Models (LLMs), specifically Mistral LLM, through integration external knowledge bases. We developed a novel methodology for dynamically incorporating real-time information from diverse sources, aiming to address inherent limitations LLMs rooted their training datasets. Our experiments demonstrated significant improvements accuracy, precision, recall, F1 score, alongside qualitative enhancements response relevance accuracy. The research also tackled computational challenges integrating knowledge, ensuring model's efficiency practical applicability. work not only highlights potential bases augment capabilities but sets stage future advancements creating more intelligent, adaptable, contextually aware AI systems. findings contribute broader field NLP by offering insights into overcoming traditional LLMs, presenting step toward developing systems with enhanced real-world applicability accessibility.

Язык: Английский

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A Multimodal Approach to Estimate Large Language Model Improvisational Capabilities

Опубликована: Май 10, 2024

Evaluating the improvisational capabilities of large language models (LLMs) like ChatGPT-4, Mistral, and Anthropic Claude across textual, visual, psychological domains provides critical insights into their functionality potential applications. The research demonstrates significant variances in ability these to generate creative, contextually appropriate responses, visually coherent images from textual descriptions, emotionally nuanced interactions. ChatGPT-4 excelled improvisation, showcasing its capacity produce linguistically rich innovative content that pushes boundaries traditional text-based AI Mistral distinguished itself generation visual content, effectively translating abstract prompts detailed relevant images, indicating utility creative design fields. performed exceptionally well adaptability, interpreting responding emotional cues with a high degree empathy accuracy, suitable for customer service therapeutic findings underscore diverse LLMs, highlighting transform industries require understanding complex content. Future should focus on enhancing reliability varied scenarios, improving ethical deployment, exploring hybrid approaches leverage unique strengths.

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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A Comparative Analysis of Cultural Alignment in Large Language Models in Bilingual Contexts

Опубликована: Июнь 10, 2024

Artificial intelligence (AI) systems, particularly those capable of natural language processing, are increasingly becoming integral to diverse aspects human life and interaction. Understanding the cultural biases embedded within AI, especially in how it aligns with specific values, is crucial for ensuring its effective equitable deployment. This research examines alignment AI-generated responses mainstream Chinese such as Confucian harmony, Daoist balance, collectivism, respect authority, family-centric principles. By analyzing both English, study highlights discrepancies inherent AI offering valuable insights into their implications development. The findings reveal that while demonstrates general significant variations exist between contexts, emphasizing importance linguistic specificity interactions. Quantitative metrics thematic analyses demonstrate necessity culturally aware contributing broader discourse on ethical development providing guidance creating more inclusive adaptable systems.

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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