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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Equipping Llama with Google Query API for Improved Accuracy and Reduced Hallucination

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

Опубликована: Март 6, 2024

Abstract This study investigates the integration of Llama 2 7b large language model (LLM) with Google Query API to enhance its accuracy and reduce hallucination instances. By leveraging real-time internet data, we aimed address limitations static training datasets improve model's performance across various processing tasks. The methodology involved augmenting 7b's architecture incorporate dynamic data retrieval from API, followed by an evaluation impact on reduction using BIG-Bench benchmark. results indicate significant improvements in both reliability, demonstrating effectiveness integrating LLMs external sources. not only marks a substantial advancement capabilities but also raises important considerations regarding bias, privacy, ethical use internet-sourced information. study's findings contribute ongoing discourse enhancing LLMs, suggesting promising direction for future research development artificial intelligence.

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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Efficient Large Language Model Inference with Vectorized Floating Point Calculations

Опубликована: Июнь 13, 2024

The development of highly sophisticated language models has revolutionized various natural processing tasks, demanding efficient inference processes to ensure real-time responsiveness and minimal computational resource usage. Vectorized floating point calculations present a novel significant approach enhancing the efficiency model inference, leveraging parallel capabilities achieve substantial performance improvements. This article details implementation vectorized within GPT-Neo, demonstrating notable 12\% increase in speed through comprehensive benchmarks datasets. evaluation highlights optimized model's ability reduce time, throughput, lower memory usage energy consumption without compromising accuracy. findings reveal potential operations enhance scalability operational advanced models, paving way for more responsive resource-efficient AI applications across diverse deployment scenarios.

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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Designing Incremental Knowledge Enrichment in Generative Pre-trained Transformers

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

Опубликована: Апрель 1, 2024

Abstract This article presents a novel approach to Incremental Knowledge Enrichment tailored for GPT-Neo, addressing the challenge of keeping Large Language Models (LLMs) updated with latest information without undergoing comprehensive retraining. We introduce dynamic linking mechanism that enables real-time integration diverse data sources, thereby enhancing model's accuracy, timeliness, and relevance. Through rigorous evaluation, our method demonstrates significant improvements in model performance across several metrics. The research contributes scalable efficient solution one most pressing issues AI, potentially revolutionizing maintenance applicability LLMs. findings underscore feasibility creating more adaptive, responsive, sustainable generative models, opening new avenues future advancements field.

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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Efficient Conceptual Knowledge Removal in Large Language Models: Methods and Evaluations

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

Опубликована: Окт. 8, 2024

The increasing use of deep neural networks has led to models that accumulate vast amounts knowledge from their training data, often retaining outdated or biased information needs be selectively removed. Novel techniques are required efficiently erase specific conceptual these while maintaining overall performance and avoiding computationally expensive re-training processes. This paper introduces a scalable framework for removal through targeted weight modification sparse fine-tuning, demonstrating how representations can isolated erased without significant degradation the model's broader capabilities. methodology achieves high precision in suppression by leveraging probing gradient-based optimization, ensuring minimal disruption general task performance. Extensive experimental evaluations confirm effectiveness proposed approach, highlighting its application scenarios where adaptive model refinement is essential both accuracy ethical integrity. Contributions field include development flexible efficient mechanism erasure, applicable across various architectures, minimizes computational overhead enhancing responsiveness dynamic requirements.

Язык: Английский

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