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

---

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

---

Mitigating Hallucinations in LLM Using K-means Clustering of Synonym Semantic Relevance

Published: June 12, 2024

Language models are prone to generating hallucinations, which significantly undermine their reliability and usefulness in critical applications. Introducing a novel approach that combines semantic relevance scoring with K-means clustering, our methodology enhances the model’s accuracy reduces occurrence of hallucinations. By integrating these techniques, model can prioritize contextually appropriate synonyms, resulting more coherent factually correct outputs. The experimental results demonstrate substantial improvements accuracy, relevance, marked reduction hallucinations across various tasks. Comprehensive evaluation using diverse metrics demonstrates robustness effectiveness modifications, highlighting potential for practical deployment applications where paramount. This study affirms viability combining clustering techniques enhance performance language models, contributing development reliable effective wide range

Language: Английский

---

Enhancing Explainability in Large Language Models Through Belief Change: A Simulation-Based Approach

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

Published: Aug. 20, 2024

Artificial intelligence systems, particularly those deployed in high-stakes environments, require a high degree of transparency and explainability to ensure that their decisions can be understood trusted. Traditional approaches enhancing often rely on post-hoc methods fail fully capture the internal reasoning processes complex models. In this research, novel integration Belief Change Theory was employed address challenge, offering systematic framework for belief revision directly influences decisionmaking process model. The proposed methodology implemented Llama model, which modified incorporate mechanisms capable handling contradictory information generating coherent explanations. Through series simulations, model demonstrated significant improvements consistency, accuracy, overall explainability, outperforming traditional models lack integrated management systems. findings highlight potential not only enhance AI systems but also provide foundation more dynamic interactive forms interpretability. research opens new avenues development are both powerful accountable, paving way adoption critical decision-making contexts.

Language: Английский

---

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

Published: June 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.

Language: Английский

---

Cross-Lingual Factual Accuracy and Ideological Divergence in Large Language Models

Published: June 10, 2024

The novel concept of cross-lingual content factual accuracy verification explores the consistency and reliability responses produced by such models when posed with identical questions in English Chinese. This study meticulously analyzed performance ChatGPT Google Gemini, revealing high alignment but notable divergences ideologically sensitive areas, attributed to cultural ideological biases training data. A comprehensive methodology incorporating both quantitative metrics qualitative assessments was employed evaluate capabilities these models. results demonstrate potential language multilingual applications while highlighting critical need for bias mitigation strategies. implications extend enhancing development deployment AI systems diverse contexts, emphasizing importance neutrality handling information. research contributes significantly understanding strengths limitations verification, providing a foundation future improvements methodologies applications.

Language: Английский

---

Retrieval Augmented Generation via Context Compression Techniques for Large Language Models

Published: June 17, 2024

Natural language processing has seen lots of improvements, yet optimizing large-scale models to efficiently handle vast amounts contextual data remains a critical challenge. The novel approach presented integrates advanced context compression techniques with Retrieval Augmented Generation (RAG), significantly enhancing computational efficiency and the accuracy generated outputs. Through series experiments, study evaluates impact token reduction, embedding optimization, hierarchical attention mechanisms on model performance. findings demonstrate that reducing redundant information while maintaining essential elements improves both quality Additionally, integration dynamic memory networks sophisticated retrieval provides robust framework for augmenting generative capabilities external knowledge. Comprehensive evaluations highlight balance achieved between performance resource utilization, underscoring feasibility effectiveness proposed methods. This research offers substantial advancements in optimization models, providing valuable insights into their applications.

Language: Английский

---

Dynamic Contextual Alignment Mechanisms for Improving the Internal Representational Consistency in Large Language Models

Published: Nov. 18, 2024

The increasing complexity of language models naturally demands innovative approaches to maintain internal representational consistency. This paper introduces Dynamic Contextual Alignment Mechanisms, a novel framework designed enhance semantic coherence within large models. By integrating adaptive recalibration strategies, the proposed mechanism aligns intermediate representations across multiple layers, thereby reducing contextual ambiguities and improving interpretative processes Comprehensive evaluations demonstrate significant reductions in perplexity attention entropy, alongside improvements scores, indicating mechanism's efficacy refining understanding. Comparative analyses reveal that, unlike traditional methods relying on fine-tuning or auxiliary this approach inherently enhances alignment without substantial computational overhead. findings potential Mechanisms advance robustness adaptability diverse applications, addressing fundamental challenges setting foundation for future developments field.

Language: Английский

---

Adaptive Neural Contextualization for Expansive Knowledge Representation

Published: Nov. 25, 2024

Adaptive approaches to context modeling have emerged as critical mechanisms for addressing the limitations of static representation techniques, particularly in tasks requiring complex understanding linguistic dependencies. The proposed framework introduces a dynamic contextualization mechanism that enhances representational capabilities transformer-based architectures through iterative refinement context-sensitive embeddings. Quantitative evaluations demonstrated significant improvements accuracy, contextual coherence, and perplexity reduction across multiple benchmarks, establishing robustness approach under diverse input conditions. Qualitative assessments highlighted framework's ability maintain semantic alignment domain-specific tasks, within highly specialized or noisy datasets. methodology incorporated adaptive layers seamlessly into an open-source transformer model, enabling efficient long-sequence processing without imposing excessive computational demands. Cross-lingual further validated its capacity generalize effectively typologically languages, highlighting potential multilingual applications. integration hierarchical attention facilitated capture long-range dependencies, while cross-attention modules ensured precise with task-specific queries. Results also robust performance adversarial scenarios, showcasing adaptability unstructured incomplete inputs. Memory utilization analyses revealed maintained scalability large datasets, balancing efficiency enhanced metrics. redefines boundaries dynamically adjust representations, offering scalable solution challenges. These findings establish Neural Contextualization foundational innovation addresses gaps current methodologies advancing field language efficiency.

Language: Английский

---

An Information Reliability Framework for Detecting Misinformation based on Large Language Models

2021 IEEE International Conference on Big Data (Big Data), Journal Year: 2024, Volume and Issue: unknown, P. 3599 - 3608

Published: Dec. 15, 2024

Language: Английский

---