Implementing Retrieval-Augmented Generation (RAG) for Large Language Models to Build Confidence in Traditional Chinese Medicine

Опубликована: Июнь 11, 2024

Many English-speaking individuals exhibit skepticism regarding the efficacy of traditional Chinese medicine (TCM), a bias often embedded in training data language models, leading to prejudiced outputs. Implementing Retrieval-Augmented Generation (RAG) within Llama model provides novel and significant approach mitigating this through integration external, credible sources. The methodology involved collecting diverse dataset, preprocessing indexing it, then integrating it with enhance response generation. Quantitative qualitative analyses indicated improvements confidence scores, sentiment balance, content accuracy TCM-related responses, demonstrating effectiveness RAG reducing biases. iterative fine-tuning process further refined model's ability produce more informed, balanced, unbiased study highlights potential fairness reliability contributing equitable representations culturally practices.

Язык: Английский

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Quantitative Analysis of the Relationship Between Optimal Learning Rate and Batch Size Scaling in Large Language Models

Опубликована: Июнь 13, 2024

The rapid development of natural language processing has led to the emergence sophisticated models capable performing a wide array tasks with human-like proficiency. Identifying optimal relationship between learning rate and batch size is crucial for enhancing efficiency effectiveness training these models. Through systematic experimentation such as Baidu Ernie, Meta Llama, Moonshot Kimi, this research demonstrates linear hyperparameters, providing practical framework their adjustment. Results indicate that appropriate scaling rates sizes can significantly improve efficiency, model accuracy, convergence time. findings offer valuable insights into dynamics training, presenting scalable approach reduce computational costs enhance robustness, thereby contributing broader field artificial intelligence.

Язык: Английский

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

Authorea (Authorea), Год журнала: 2024, Номер unknown

Опубликована: Окт. 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.

Язык: Английский

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Dynamic Neural Embedding for Contextual Regeneration in Large Language Models

Опубликована: Ноя. 25, 2024

A novel embedding methodology capable of dynamic realignment with evolving contextual inputs is introduced, addressing longstanding challenges in maintaining coherence across extended sequences. The proposed approach integrates a real-time regeneration mechanism, enhancing the ability language models to retain semantic consistency through adaptive adjustments. By incorporating feedback-driven token realignment, framework ensures logical continuity generative tasks without incurring significant computational overhead. Quantitative analyses demonstrate gains context retention and fidelity multiple benchmark datasets, marked reduction error propagation during sequential interactions. system’s scalability evident its efficient handling input lengths, robust performance such as summarization, machine translation, domain-specific text processing. Through integration kernel-based approximations hierarchical attention mechanisms, optimizes resource usage while sustaining high accuracy complex linguistic representations. Comparative studies highlight model's adaptability specialized vocabularies, particularly fields requiring understanding. robustness design further validated low-resource ambiguous scenarios, where conventional methods exhibit degradation. Error analysis demonstrates effectiveness mechanism reducing cumulative inaccuracies over iterative Results confirm framework’s capacity balance depth, setting precedent for future advancements embedding-based architectures. redefines boundaries model capabilities, achieving an unprecedented synthesis efficiency, adaptability, coherence. findings offer substantial contributions evolution processing architectures, establishing innovation.

Язык: Английский

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