Comparative Analysis of Finetuning Strategies and Automated Evaluation Metrics for Large Language Models in Customer Service Chatbots

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

Published: Aug. 13, 2024

Customer service chatbots have become integral to the efficient operation of many businesses, offering scalable solutions handle vast volumes customer interactions. However, ensuring that these generate accurate, contextually appropriate, and coherent responses remains a significant challenge, particularly as complexity queries increases. The research presented introduces novel approach optimizing chatbot performance through an in-depth comparison various finetuning strategies evaluation metrics, demonstrating Domain-Adaptive Pretraining (DAPT) provides superior accuracy, robustness, relevance in scenarios. A comprehensive experimental analysis was conducted across three distinct large language models, revealing while DAPT excels producing high-quality, resilient responses, parameter-efficient methods offer resource-efficient alternative suitable for environments with limited computational capabilities. study’s findings critical implications development deployment chatbots, emphasizing need careful selection aligned specific operational requirements.

Language: Английский

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Automated Comparative Analysis of Visual and Textual Representations of Logographic Writing Systems in Large Language Models

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

Published: Aug. 16, 2024

The complex nature of logographic writing systems, characterized by their visually intricate characters and context-dependent meanings, presents unique challenges for computational models designed primarily alphabetic scripts. Understanding the ability LLMs to process scripts across visual textual input modalities is essential advancing application in multilingual contexts. novel approach presented this study systematically compares performance when interpreting as both data, offering new insights into semantic consistency accuracy model outputs these modalities. findings reveal critical disparities performance, particularly highlighting models' tendency favor inputs, which suggests need further refinement multimodal processing capabilities. Through detailed analysis error patterns, similarity, complexity, research demonstrates importance developing more robust versatile LLM architectures capable effectively managing inherent complexities systems. conclusions drawn from not only provide a deeper understanding limitations current but also set stage future innovations field, aiming enhance generalize diverse linguistic structures types.

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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Assessing Reasoning Capabilities of Commercial LLMs: A Comparative Study of Inductive and Deductive Tasks

Authorea (Authorea), Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 6, 2024

Artificial intelligence has revolutionized various fields through its ability to process and generate human-like text, leading significant advancements in tasks requiring language comprehension generation. However, the evaluation of fundamental reasoning abilities within commercial LLMs, specifically inductive deductive reasoning, remains crucial for understanding their cognitive capabilities limitations. This research provides a comprehensive assessment ChatGPT, Gemini, Claude, using meticulously designed set evaluate performance. The methodology involved selection diverse datasets, design complex tasks, implementation robust automated testing framework. Statistical analyses, including ANOVA regression techniques, were employed rigorously compare models’ performance across different tasks. Results indicated that ChatGPT consistently outperformed other models, particularly excelling high precision recall, while Gemini Claude exhibited variability capabilities. study highlights strengths weaknesses each model, offering insights into relative potential areas improvement. Implications AI development are significant, emphasizing need tailored model designs continued innovation training techniques enhance abilities. contributes broader providing foundation future developing more capable reliable intelligent systems.

Language: Английский

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