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

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

Опубликована: Авг. 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.

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deep neural networks has led to models that accumulate vast amounts knowledge from their training data, often retaining outdated or biased information needs be selectively removed.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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Dynamic Contextual Aggregation for Semantic Fluidity in Natural Language Processing

Опубликована: Ноя. 18, 2024

The rapid expansion of computational linguistic capabilities has demonstrated the necessity for models capable adapting to dynamically evolving contexts within diverse textual environments. Addressing this challenge, Dynamic Contextual Aggregation framework introduces a groundbreaking approach that surpasses limitations static and traditional contextualization techniques by enabling semantic fluidity adaptability through real-time contextual integration. framework's theoretical underpinnings, grounded in dynamic aggregation principles, provide robust mechanism representation, enhancing coherence relevance generated content across varied tasks. Empirical evaluations demonstrate significant improvements accuracy, adaptability, robustness, particularly complex noisy language processing scenarios. findings affirm utility novel advancing contemporary while establishing foundation further exploration modeling. Through combination innovation practical evaluation, research contributes step forward pursuit more contextually aware flexible systems.

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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