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

Lucas Lisegow,

Ethan Barnes,

Ava Pennington

и другие.

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

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

Lucas Lisegow,

Ethan Barnes,

Ava Pennington

и другие.

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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